{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinearRegression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk9dNKUdf7_o"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression,Ridge,Lasso,ElasticNet,LogisticRegression,Lasso\n",
        "from sklearn.datasets import make_regression\n",
        "from sklearn.datasets import make_blobs"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "_EPXSRt3gPVO",
        "outputId": "95441df3-10cb-4951-ba62-44c6f8854290"
      },
      "source": [
        "# load and visualize data from sklearn\n",
        "# n_features means the dimensions of data instead of number\n",
        "# X-2D array Y-list\n",
        "X,Y = make_regression(n_features=1,noise = 10,n_samples=1000)\n",
        "plt.xlabel(\"Feature-X\")\n",
        "plt.ylabel(\"Target\")\n",
        "plt.scatter(X,Y,s=2) # s equals the size of marker(here is blue dots)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sT-x2GrLhN-I",
        "outputId": "d6168e30-42eb-49bd-c274-92fdc6550a68"
      },
      "source": [
        "# model\n",
        "learner = LinearRegression()\n",
        "learner.fit(X,Y)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cDY6V6wXciw"
      },
      "source": [
        "The weight and the number of it is equal to the number of the dimension of data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sppfCYrziNOK",
        "outputId": "e7f72d49-c3d5-4ec0-ead1-195cf00ef280"
      },
      "source": [
        "learner.coef_ "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([69.59604974])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSn1VmLaEhcZ",
        "outputId": "09e8e334-180a-4e6b-943d-fcd336084c25"
      },
      "source": [
        "learner.intercept_ # bias always one"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.2743309880881992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTlIqXgGElIH"
      },
      "source": [
        "pred = learner.predict(X) "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS-fciaYErbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "5170c727-b369-4a71-f4fb-009a4bd5d79d"
      },
      "source": [
        "# visualize the result\n",
        "plt.scatter(X,Y,s=5,label = \"original\")\n",
        "plt.scatter(X,pred,s = 5,label = \"prediction\")\n",
        "plt.xlabel(\"Feature-X\")\n",
        "plt.ylabel(\"Target-Y\")\n",
        "plt.legend() # make labels show inside\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoL81uFxVXDc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc758fb5-2ce1-4222-99d4-e6728f3f69b0"
      },
      "source": [
        "ridge = Ridge(alpha = .1) # RidgeRegression\n",
        "ridge.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])\n",
        "learner.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1x7hWJTuR7p",
        "outputId": "74cc85b2-022a-4153-c726-19b586f2a385"
      },
      "source": [
        "ridge.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.44186047, 0.44186047])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iL5T7fNruVvI",
        "outputId": "b4f87643-e7fa-41b8-ec9c-5a158f6541c2"
      },
      "source": [
        "learner.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.475, 0.475])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "4wK-5Q1XuagG",
        "outputId": "b23d1da7-1475-4de9-de4a-38d4d78819b1"
      },
      "source": [
        "# effect caused by outliers\n",
        "outliers = Y[950:] - 600\n",
        "Y_out = np.append(Y[:950],outliers)\n",
        "plt.scatter(X,Y_out,s=5)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD4CAYAAAAEhuazAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXzc1Xnv8c8zo8WLdrxiWzbJNRAWg2XhhTRtwpKQpDcOvSW1nbRpQjCkQLq+AoS2aXZy294mQAIxhHubBuzSpml8W26JgbQNYGNLNnZsVhUs2Q7GsrVYkm3JmnnuH/Ob8Wg0WmekkTzf9+ulFzPn95uZM4Q8vzPPeX7nmLsjIiL5JZTrDoiIyPhT8BcRyUMK/iIieUjBX0QkDyn4i4jkoYJcd2A4ZsyY4YsWLcp1N0REJpX6+vqj7j4z3bFJEfwXLVpEXV1drrshIjKpmFnjQMeU9hERyUMK/iIieUjBX0QkDyn4i4jkIQV/EZE8pOAvIpKHFPxFRCaoaNRp7uhmLFZfnhR1/iIi+SYaddY+tI36xlaWLaxk400rCYUsa++vkb+IyATU3NlN3f4WeqNOfWMrx7p6svr+Cv4iIhNMNOrcvnEXkSDbU1NdwYySoqx+hoK/iEgg0xx7tnL0x7p62NnYCkA4ZNy/rgaz7KV8QDl/EREg8xx7NnP0M0qKWLawMvFeM0uLR/U+g1HwF5FJKRp1jnX1MKOkKCuj4mNdPdQ3tvbJsY8k6Gb6+mRmxsabVmb1+6XKStrHzB4xsyNmtjeprcrMtpjZ68E/K4N2M7N7zazBzPaYWU02+iAi+SM+yl71jadZs2Eb0Wj6NEtqGmawtEx8tF0QMpYtrEybY8/09SMRChkzS4vHJPBD9kb+/we4H/hBUtudwNPufo+Z3Rk8vwP4ILA4+FsBPBD8U0RkWIYzyk5Nwzx644rY86bY879fv6pPWsbMePTGFTQ0d3L+7JJ+QTddWifel/jofKxH69mUlZG/u/8n0JLSvBr42+Dx3wIfTWr/gcdsAyrMbG42+iEi+WE4o+zkC0Td/ha2vnmUHY2tRB127G/l7eOn+pwfjTof//4L/Pp9z7L2oRf6/ZpIveA0d3azZsNWVnz9Ka7/7nNEItExH61n01hW+8x297eCx4eB2cHjecCBpPMOBm0iIsPiDveuXcrzd17FpvUr0wbb+AUibDCtuIDffaTvhlBtJ/rWzaf7NZHu/eIXHHdn+/7YxeTFA+385oPPD5h+mojGZcLX3d3MRvRvxczWA+sBqqurx6RfIjK59PZGee1IB1/8yV52HWhPpF/isT91EnjjTSt57e0OPnzfs0SS8vSlxQVcMKc0cX7VtELcnZrqCnY2taX9NZGa1mnu6O5zfPeB9owmecfbWAb/t81srru/FaR1jgTth4AFSefND9r6cPcNwAaA2trayXM5FZEx0dsbZelXt9BxqjfRVre/haOd3cwqmzJgqeUFc0qpDcoma6or+NJHLuaCOaWAsfahbdQ1tjKtKMyJ7l6WLaziuTuvYtYAqZt4WgdgZmkxyxdVsn1/rB6/dlHmk7zjaSyD/2bgk8A9wT9/ktR+m5ltIjbR256UHhKRs1gm5ZkNzZ19Aj9AxOG2jbvYFIzIU9M250wv4lhXDz/89HL+62gX588uIRSKZbubO7qpb2wlEvXE++5saiVkNqy+mRmb1q+iuaMbMyZNrj8uK8HfzDYC7wVmmNlB4IvEgv7jZnYj0Ah8LDj9CeBDQANwAvhUNvogIuNrqECeenykN0Elp2RaTpxm8azpTC8O09Ud6XPeziDQp94YVTWtsN/IvnZRVeJz4+f3HfmPbPQeChmzy6cM/1/aBJKV4O/uawc4dHWacx24NRufKyK50dsb5WMbtrL7QFufgBqXLtAf6+qhbn8LEY+lawbLj8dfv2N/C1OLwpw6HaV2YSV1d17Nsm88zYmeCGED7Ey1T2pO/mhnT7+RfXJZaPL58QvMZCjRzBbd4SuSx4abhkk+LxJxPvrAc+w9dBxIH8j7lUV2dOM404oL6DjVy5SiMJVT+4ef+Oe4O3VBWWZ8pF/X2Epj20m6e6MAOPDE7b/CBXNKE78skr/LcEb2qTn8fKLgL5KnhkrDJKdd1j38AvWNrVw+v5zOngivHO5InHfZgv4rTlZNK+TSeeXsOdTO0uoKbvlhHbsPtBMNjnd1R/joA8/zk997N+FwqF9/aqorWDKvnF0H2hLvuWR+OefPLklM3i5bWNkn8Kf7Lvk8sh+Kgr/IWWQkE6rpRufxXHgk4om0zpL5Few51E4k6tQ1tfV5DwMe+/RyjnbGAuzRrh6ikSg3/10de34Zu0DsPdjGyd7+BXt7Dx3nfzzwHF//jSVcOKeUY12nz/SnqY1Lzy1NnHv5/HIe/HjNgHfRDnTHbz6P7Iei4C9ylhjuhGr8AnHO9MLEBGlNdSW3b9zJzqY2aqorOB3xxKg7efSdyoGPPriV1490MrUwTGd3b79zkgP/xeeW4u689FYnAC8ePM6H7n2W6UVhfvqH76GmuoL6pjaWzCtn98HY54aIVda8+5s/S3yv1ECeOtk7mUouc0XBX2QCGk1JZJ/lDBpbE/Xv8feLL0j2uU27EjcyPXrjClpPnsbdWfmNp4k6bN/fSngESxHHU0DpAn8yA/b9soPLF5QztQBOJp3e1RPh3d/8d6YXhYhEnVcPtxO/WfayBWd+eQy0js9kW1dnItBmLiITzGArVg61qmRNdSUAkahz0w92EIlEiUadNRu2seIbT7PynmfYvj92gdj+ZguvH+lgRkkRlVML+/UhG5JDcPwdXzzQ3ifwJ+vqic0KnDgdOzsEPPiJGmqHsVrmZFpXZyJQ8BfJkYEC+UBrzAx0UejtjfLSL9s5cvwU31pzWeJ9dh88zm9891n+be9b1De19vt8Bz5477P85nef44XGFpLjfTZC/6M3XsErX3o/UwsHDzMXzilJPJ5W1Pfcy6srmFU2hY03rWTrXVcPuI6PjJzSPiI5MFh+Pt3NSvGLRHJa50jHKdzh2r/5DzqDcsh3zZ7e53N2H+rg9x7bNWhf6g+084mHtw/Z58vmlbE7KO8EmF4Upqsn9rlG7IIRgkRFz7ef+S/uW7uUU6ejqW/F9KIwp3qjse/+mRUc7ezBLFYldMOGbew52M6S+eX84y2rMLPEHbSSPQr+ImNgqJx9utF95dRCXj58nOMne/jhp6+g5WQvvacjfOT+Z3n5cAc11RVcfG4Zuw/G8t/v+6t/p7s32mfE/vLbXWPyfaYXh3nwE8t49//8GVGPBfun/+jXMDNaurr58H3P4h67AITNiAQXKtxZfl4VO/a3MKUwxImeKDXVFTy+fiWtJ3sT/36S75L90S1XKnc/DhT8RbJsOFU3VdMKuWReGS8eaOfCOSWUFBhLvvQkJ4JRcghYuqCc+gPtidfUNfatujmZZkQ9Vk72RAiHQ1yxqCrxvWaXT8HMmFVWnGivqa4AYpPGkahz+8ZdPPaZlbSePN2v1n5maTjtZyWXZ8rYUfAXybLUqptXDx/n/NmlicB3+nSUj37357x0ODZK3/vLDi7+8pY+I/go9An8uTK9OMzJnghXLKpiZmlx2oqa1EqbIx3dXPmNp4k47Gxqo/XkadXaT0AK/iJkvhl48utjd7eWsetALD3zwXufZWqhceq0U7OgnJff7uBET99Rey73AFlybglvHDuZmDeIe+wzy1lx3jl9RusD5d6TR+uzSoupTfqFoJr7iUnBX/Je6rIC316zlHAQzNwZcHmA3t4oDc2d/LeZ0/n497dT39jK0gXlnDgdZd8vj/f5jJNB6WKuR/NTC6zf3bbf+50rmFkyhVffPs4ND27lxOkopVMKWPmOcwiFQiMeravmfnJQ8Je8d6yrh7pg9cft+1tZdc8zAFyxsJLTkQgvHjyeqE65ZG4pX7/+UiqnFXLtt/6Trp5oYlTvwI7Gge+GzQUDphaGOHE6SklxmJ13X0NdUyvrkqp7wqEQoZDx5X95me6Ic+m8Mn782SsT696PhvL2E5+Cv+S1aNSJRqNMLQz1S3vsaDxTGx8vadx96Dgfvv+5PufFR/UTUShkPPPH76Xt5OnERiar3jmD5YuqqG+KpWVmlhb3Wf745bc6aD3ZO+CErJwdFPzlrDdQPj+e7omP+ieLC+eU8MbhTnqGPpUl88uZXT6FORVTE22xHahWpl3+WHn6/JGz4G9m1wHfBsLAw+5+T676IpNfugAfv4P29o07qW9sZcn8Ch74RA2zy6YQiTgv7D/G9jdbsnI361iK30AFcMm8Mjbf+m4iEef6B5/n5bdi9f9f+u8X8cXN+6hvbGNqcZgT3RGWVlckbpJKlZqWUZ4+/1i6NULG/EPNwsBrwLXAQWAHsNbdX0p3fm1trdfV1Y1jD2UiGTCwd3ZjwDnTixLrzS9bWMnffWo5rzV38Gf/vC+xIFiyZQvKeOXtrkQqZyIJW2xf2riphUY4dCYlVVtdweO3XEko1H/zktRtDxXExczq3b023bFcjfyXAw3u/gZAsJn7aiBt8Jf8kC7I9/ZGuWHDVvYcbKc2uGEqGnVu+N7z7AoqZy6fX8aeQ8eJemy3p6Vf3TJoYK8/cHzAY7lUW13B/euWctVf/0fiZq+Tpx3jzHfZdbA97Vr1kN+7UsnI5Wpht3nAgaTnB4O2BDNbb2Z1ZlbX3Nw8rp2T8Zdu0bJYkN/Krqa22EYija28ffwU1z/wXCLwQ2xN+PjgPhL1foG/OEfzlgvLh//BYYPvfmIZ4XCsqihu6YJyahdVJp4rHy/ZMmEnfN19A7ABYmmfHHdHsiB1H9iG5s5EBUryXbE73mzhyPGTmIX6bCRy6bll3PSDHez9Zccgn9Jfd46yO3d8eMmgi6otD4J6fG39+Gj9ikVV1DW2smR+OT+6ZRVgiRSXliyWbMlV8D8ELEh6Pj9ok7NQ8sTrzqY2Lp9fxsuHO+nqiTCtKMTOL1yDu3PZvFLqDxwnClz5zZ/xn3/ya33ep/t075gtXJYt8cnZ6cVhrn3XTEqnxDYsDxm4wxWLKrl37VJC1vcmsuRUV7qJ19llUwb+UJFRyNWEbwGxCd+riQX9HcA6d9+X7nxN+E5cg5VRxicf4+WUA1VThixWbVIYsj4pj0XnTKVqagEvHuzg0pTlhHPhHVVFvNHSk1i2OLkKB2KTszu/cA03PPwCL/3yOFcsquLvPrWcN4518d9mTu+ziqXIeJhwE77u3mtmtwFPEiv1fGSgwC8TV99lESq5b91SZgWj2bUPbaNufwvvmls6ZJom6oB7v6qc/cdO0mSAwcne8VvBEuC765ayoGoq//3+5xNthzti209FgQtnT+fVpF8hUwtD/OKL19J6MsIrb3UQdahvbKW9u5cL55YB6KYpmVBytpOXuz/h7ue7+zvd/Wu56oeMXDyNc7SzO5Gn376/hVXfeJrf2rCN5s5uduxvIeL0C/xFI/wvLuqxv9fe7hxxP6cUwM//+D1MG2InqVQlxWGuu2QOl8yrYPmiKsIh45J5ZYkKHIDX3u7i8uoKQharvd/7F++noKAgcbPUUFsOiuTahJ3wlYkhXS15fFS/ZH45NdUV1AfVOFGH7W+2EOmNMKUwzIk05ZY94zSAv3BOKf/7d2tpO3G6T9AeyDvOmcIPb1xJ+6leLphTmljXJn4nbNW0Ai778pYz9faLYmWnqfX0ullKJouc5PxHSjn/3IgH+h37W7jo3DL+6eZVNBzt4sP3PpvIdV98bilfv/4SVn9na+J1F59byr4RVuSkmlJgnOrN7L/NsEFNdQWvHO6ko7uX0ikFXDBrOnVNfVfWNOD1r36AgoLBx0K9vVFeO9LBOdOLmFU2RYFdJrzBcv4K/nlmsHXrU++aff1IJx/89s8TgT5k6dedn1aUfpQ/WvGJ1NG+b/JEbEHI+Pkd76P9RGxhMzCOdJzis4/u5MWmNi6YU8K/3PbuIQO/yGQ04SZ8JTcG214wGnXWbNjK9v2xlSxLiws4cTpCcUEoUYEzULVONgM/nAncp05H+lXUxBnw3Od/lc/9/Z5+2xv+6+3v5i/+70uJ+vk5ZVOYW35mYbM55VO1T6zkPQX/PBAf7bs7dcFEbN3+lsQyAXBm68G4ju5YZUsuVruMb3dbu6gKj0b7rJFvwDtnTOO/jp7gD/9hL/evreHKbz6TuDBdvqCcd51bzqb1qwYN7lpvXvJdzqp9ZGz09kZ55a3jRKPBaD0Y0a/8+lPc+mg9U4ti5YbTiguomlaYqNypnFrAkvnl49bP4kH+y4s6XDS3jI2fWcH965YRTtr8/OJ5Zbx57AROrJQyFDKuWFSVyO//02evxMwSwV2jepH0NPKfpAZaBG3pV7fQcSo2uVn/hWt4/WhnIpWTPILu6u7laFcPn9u4i7rGVqYVhuno7uXiuaVs+O0arvqr/6A7w8qcxTOm8vrRk2mPDfXeLx/uoOXEaWaVFVO7sDKx3ME/3rySdQ9vT6SuBtpUXEQGp+A/SSQH++SbqC5bUME/3LyKcDhEQ3MnHadi6ZqOU7185LvP8crhvlU38UnbaUVhXj3czo79LUT9TJpn31sd3PzDXSMK/AYsW1hBJEqftXgqS6ZQO62Iuqb2tHfDJu+AlVohFK+RT1c6mfp8oE3FRWRgqvaZIAZbJqG5o5vbHqtPTGDet7aGK+95OrHu+6Xzyvjn33s3ZrDkS1vo7O4dcKJ0uC6bV8ruQwOXayYqcgqNn/3J+5hVNgV3eOXwcX79vmeJeqzS5rk7ruKN5k7WPvxC4rWP3bicKxZVcvlXn6KrO0LplAJ2/ek1HOs6jeOJdW80ihfJzGDVPsr5j4N4AE++0Ca3pVvOOH7O2oe2seqep9nR2EbEYfv+VhznsgUViff6xaHj3PC9rfT0RBO5/kwv6V+7fglLF1QQNuOiuSV9jj1243Licbm71wmFQok8+7vmlnHFoqrEHa6zyopZ8Y4qSqfEfmSWTilg5TvPobCwgN1/9n7+7fffw+4/v5aCgnBsu8HyqaqhFxkHSvuMkeSFzZJ3mdp400oA1mzYmmi7b11NYpmE+sbWRBVOvAInteAmZMbf37SSy76yJVFm+eKBNj764HMD3s06rcA4Edw0NVC9ftz0ohBV0woTKZyX3urk8vll/OLQcZYtrGTlO8/hikVVafd7TZ+WMXb96bV9lnAGKCgIJda9EZHxpeA/BpLr6S+dX86eA7FRe31jK82d3Rzr7E5Mwm7f34q7p908O75OzI79LbH3dZheFMbdaT15mpNJ9fVTCsO8crj/+jdLF1Twvd9eRnNnNx++99nE+0DsZ98PbryCb215nV0H27l8fhndvc5Lbx3n1o0v9nmfBz9RSzgcGjDvnixdGaUCvcjEopz/MA12Z2yq5o5uVn3jaXqjTjhkXDa/nN0H2lgyv5zCcIi6/a0kj8+3f+FqqqYV0dDcmVj6N74Pa8WUArY3tvDxh7cnzg8HKZXTvZHEjlYhg8sXVLCrqY2pRSFO9ESDuYArCYfDRKNRLvtyrBIobIBZ7A7a7l6WLazivnVLMeDKe55J9HvJvDL2HGynprqS73y8Rnl4kUlGd/iOQrrqmnR3xqYTH7HHz//hp5fzsYe2sftAW590S9iMZYsqExuQ73izhWnFsSUNphcX0NXdy/Tign530Eaizs7GVp67433c/MP6Pvvbxi8YH3toG3sOtrPu4e1Bqsn46R/8Km0nelg8q4SG5i4+fO/PiTjsbGolZNav3499ZgVHO3u4feNOrrznmWF9dxGZHBT800hdBuHeNUvT5uQHkpoWOdrZwy8OtvcJ/CXFYZ7+o19jVtkUjnb2ULe/hSgkVo1MLtlMp6a6ghklxRSFw7HReJBbn1laTHNHN7842E4k6G9zZzef27irz8Xrgjml1Kbk7dOlc0IhY2dT27C/u4hMDhlV+5jZDWa2z8yiZlabcuwuM2sws1fN7ANJ7dcFbQ1mdmcmnz9WkveTrW9sxYwRr9EeChnnTC/iyPFueiMRLp1fTvKA+WRPJFElM6OkqE/1TrJpRf03AAmbcf+6GlpOnGZnU2vil8Cxrh6AfmvKG/S7eMUD/da7rmbT+pWJdE7qnbFan17k7JTpyH8v8BvA95IbzewiYA1wMXAu8JSZnR8c/g5wLXAQ2GFmm939pQz7kTXRqOPu1FRX9NlYe6R3kcaWVdjG9mCyFmKTrwUh2HWgvU8gNTP+4eZV/OaDW2OpoeD8sMFTf/gerv3Wz+lK2oV82aIzm32nmyhOHcEPdN5w1rfR+vQiZ6eMgr+7vwykCwirgU3u3g28aWYNwPLgWIO7vxG8blNw7oQI/qnbEj5351XMCkbBI72L9FhXD/VNrX3a9hxq5/k7riIUsn6B1MwoDBsYlBSFOdkToXZRFXMrprH7z97Pa0c6qJxWSDgU6jMyHygwpwb2TAK4FkETOfuMVc5/HrAt6fnBoA3gQEr7inRvYGbrgfUA1dXVY9DFM5JXvYynR+KToOkC5XAqf+Lpku1vnhn5x296SveaY1097GyKTQifPB3lXz/3Hi6YU4qZUVBgXHRu+kXXhhuYFcBFJNmQwd/MngLmpDl0t7v/JPtdinH3DcAGiJV6ZvO9B6rkqVlY2Sfdky6/Pdia+MnMjE03rYzdxTuMJQtSK23igV9EZCwMGfzd/ZpRvO8hYEHS8/lBG4O0j4mB9qAdqJLn+TveRygUGnBUnzoZPFj1SyhkzC6fMqx+KrcuIuNprNb22QysMbNiMzsPWAxsB3YAi83sPDMrIjYpvHmM+tBnLfvf+t7WxIUgtZKnproSiNXP375xF+dMTx98E5PBY1T9ojXoRWS8ZFrqeb2ZHQRWAf9qZk8CuPs+4HFiE7n/Btzq7hF37wVuA54EXgYeD84dE83BMgrxBdGaO7v7lS7OLC3mvnVLY3e9Ajub2hIlk8nivxiuvOcZcOe5O67qUyIpIjKZZFrt82PgxwMc+xrwtTTtTwBPZPK5w5UalmOrGvRPr8wqLe53w1Oq5F8MO5vaCIXSTwaLiEwGZ/UdvjNLi1m+qIr6pjO7PkH/ypfh5NtTJ2R1s5OITGZn/cJuI1mQbTzfS0RkrOX1wm7ZrG9XrbyInC20k5eISB5S8BcRyUMK/iIieUjBX0QkDyn4i4jkIQV/EZE8pOAvIpKHFPxFRPKQgr+ISB5S8BcRyUMK/iIieUjBX0QkD2W6mctfmtkrZrbHzH5sZhVJx+4yswYze9XMPpDUfl3Q1mBmd2by+SIiMjqZjvy3AJe4+xLgNeAuADO7iNgWjRcD1wHfNbOwmYWB7wAfBC4C1gbniojIOMoo+Lv7T4OtGQG2EduQHWA1sMndu939TaABWB78Nbj7G+7eA2wKzhURkXGUzZz/p4H/FzyeBxxIOnYwaBuovR8zW29mdWZW19zcnMVuiojIkJu5mNlTwJw0h+52958E59wN9AKPZqtj7r4B2ACxnbyy9b4iIjKM4O/u1wx23Mx+F/h14Go/syfkIWBB0mnzgzYGaRcRkXGSabXPdcDngY+4+4mkQ5uBNWZWbGbnAYuB7cAOYLGZnWdmRcQmhTdn0gcRERm5TPfwvR8oBrYEG5pvc/db3H2fmT0OvEQsHXSru0cAzOw24EkgDDzi7vsy7IOIiIyQncnUTFy1tbVeV1eX626IiEwqZlbv7rXpjukOXxGRPKTgLyKShxT8RUTykIK/iEgeUvAXEclDCv4iInlIwV9EJA8p+IuI5CEFfxGRPKTgLyKShxT8RUTykIK/iEgeUvAXEclDCv4iInlIwV9EJA9lupPXV8xsj5m9aGY/NbNzg3Yzs3vNrCE4XpP0mk+a2evB3ycz/QIiIjJymY78/9Ldl7j75cC/AH8etH+Q2NaNi4H1wAMAZlYFfBFYASwHvmhmlRn2QURERiij4O/ux5OeTgfi24KtBn7gMduACjObC3wA2OLuLe7eCmwBrsukDyIiMnKZ7uGLmX0N+B2gHXhf0DwPOJB02sGgbaB2EREZR0OO/M3sKTPbm+ZvNYC73+3uC4BHgduy1TEzW29mdWZW19zcnK23FRERhjHyd/drhvlejwJPEMvpHwIWJB2bH7QdAt6b0v7vA3zuBmADxDZwH2YfRERkGDKt9lmc9HQ18ErweDPwO0HVz0qg3d3fAp4E3m9mlcFE7/uDNhERGUeZ5vzvMbMLgCjQCNwStD8BfAhoAE4AnwJw9xYz+wqwIzjvy+7ekmEfRERkhDIK/u7+PwZod+DWAY49AjySyeeKiEhmdIeviEgeUvAXEclDCv4iInlIwV9EJA8p+IuI5CEFfxGRPKTgLyKShxT8RUTykIK/iEgeUvAXEclDCv4iInlIwV9EJA8p+IuI5CEFfxGRPKTgLyKShxT8RUTyUFaCv5n9sZm5mc0InpuZ3WtmDWa2x8xqks79pJm9Hvx9MhufLyIiI5PpNo6Y2QJie/E2JTV/EFgc/K0AHgBWmFkVsQ3eawEH6s1ss7u3ZtoPEREZvmyM/P8G+DyxYB63GviBx2wDKsxsLvABYIu7twQBfwtwXRb6ICIiI5BR8Dez1cAhd9+dcmgecCDp+cGgbaD2dO+93szqzKyuubk5k26KiEiKIdM+ZvYUMCfNobuBLxBL+WSdu28ANgDU1tb6EKeLiMgIDBn83f2adO1mdilwHrDbzADmAzvNbDlwCFiQdPr8oO0Q8N6U9n8fRb9FRCQDo077uPsv3H2Wuy9y90XEUjg17n4Y2Az8TlD1sxJod/e3gCeB95tZpZlVEvvV8GTmX0NEREYi42qfATwBfAhoAE4AnwJw9xYz+wqwIzjvy+7eMkZ9EBGRAWQt+Aej//hjB24d4LxHgEey9bkiIjJyusNXRCQPKfiLiOQhBX8RkTyk4C8ikocU/EVE8pCCv4hIHlLwFxHJQwr+IiJ5SMFfRCQPKfiLiOQhBX8RkTyk4C8ikocU/EVE8pCCv4hIHlLwFxHJQ5lu4P4XZnbIzF4M/j6UdOwuM2sws1fN7ANJ7dcFbQ1mdmcmny8iIqOTjc1c/sbd/yq5wcwuAtYAFwPnAk+Z2fnB4e8A1xLb9nGHmW1295ey0A8RERmmsdrGcTWwyd27gTfNrAg+OtsAAA0YSURBVAFYHhxrcPc3AMxsU3Cugr+IyDjKRs7/NjPbY2aPBJuyA8wDDiSdczBoG6i9HzNbb2Z1ZlbX3NychW6KiEjckMHfzJ4ys71p/lYDDwDvBC4H3gL+Olsdc/cN7l7r7rUzZ87M1tuKiAjDSPu4+zXDeSMzewj4l+DpIWBB0uH5QRuDtIuIyDjJtNpnbtLT64G9wePNwBozKzaz84DFwHZgB7DYzM4zsyJik8KbM+mDiIiMXKYTvv/TzC4HHNgP3Azg7vvM7HFiE7m9wK3uHgEws9uAJ4Ew8Ii778uwDyIiMkLm7rnuw5Bqa2u9rq4u190QkSFEo86xrh5mlBRhZrnuTt4zs3p3r013bKxKPUUkz0SjztqHtlHf2MqyhZVsvGklodD4XwB0ARoeLe8gIllxrKuH+sZWeqNOfWMrx7p60p4XjTrNHd2MRdYhfgFa9Y2nWbNhG9HoxM9s5IqCv4hkxYySIpYtrKQgZCxbWMmMkqJ+54x1cB7uBUiU9hGRNEaTOjEzNt60ctDXpQvOM0uLs9bv+AUonnpKdwGSGAV/EeljJLn71ItEKGSDBvOxDs7DuQBJjIK/iPQx3NH5aCZ4xyM4D3UBkhjl/EWkj+Hk7mH0+fV4cNaoPLc08heZRIaTi08+x520jwcLvO5w79qlGAwapJVfn9wU/EXGwXAnUAc7bzhpluRzaqorAWdnUxs11RWAsbNp8BRNus8YqLvKr09uCv4iY2y4ufHU8x69cQWtJ08nAmtqmqW5s5uQWZ/A2+ecplZwJ+JQ39gKZkSGyOOPtBpH+fXJSzl/kTE23Nx48nl1ja18bMPWPvXwM0qKqKmuIGywdEE5t2/c1a9ePjVfP9DjgVI0w833y+Snkb/IMI122YDh5saTz7t0fjm7D7QlRu3Huno4Z3oRYGBGbxT2NLX0OR7PzyenYkaa81cqJ38o+IsMQybr1gw3oCafd870QtZsiOfuK5hRUsTRzh52NrUSiTp7DrVz2YIK9hxs73dBSU7FmJH28WCUyskPCv4iw5DpnanDDaihkHHO9CKaO7tjDRYb6bv3/wXx2GdW0HLitEboMioK/iLDMJKyxkxWlYz/wqjbH0vpAOxMutik/oLQCF1GK+Pgb2a3A7cCEeBf3f3zQftdwI1B++fc/cmg/Trg28Q2c3nY3e/JtA8iY22w1E1qXX269NBwLwjxXxjxwB9OmXhVSkayJaPgb2bvA1YDl7l7t5nNCtovIrZF48XAucBTZnZ+8LLvANcCB4EdZrbZ3V/KpB8i6WR7Xfd44I1GnaOd3WmD/b1rlvZLD50zvWjY8wXJvzBqqiu4f12N7oaVMZHpyP+zwD3u3g3g7keC9tXApqD9TTNrAJYHxxrc/Q0AM9sUnKvgL1k1VhuLJNIyja0smV/OAx+v6RPszeiXHjraOfz5AlXbyHjJtM7/fOA9ZvaCmf2HmV0RtM8DDiSddzBoG6i9HzNbb2Z1ZlbX3NycYTcl3/Spmd/fwtHO7j6biIx2Q5FjXT3UNcYqbnY1tfHZR3dSU12RqIuP5+W33nU1m9avxIKbsEZSO6+1b2Q8DDnyN7OngDlpDt0dvL4KWAlcATxuZu/IRsfcfQOwAWJ7+GbjPeXsM1BqJ35D1Pb9sfz5bY/tBAiWOui77MF9a2uYVdY/2KZ77xklRSyZX86upjYA9hxo4/k7ryYUOnOnbWpJpUbzMhENGfzd/ZqBjpnZZ4F/8tjwabuZRYEZwCFgQdKp84M2BmkXGZHBUjtmxn1ra7jym8/EljRoajuz1EHSsgfb97dy5TefoTbl9QO9t5nxjzev4obvbWX3gTZqF1WlvXCk0kStTDSZ5vz/GXgf8LNgQrcIOApsBh4zs/9FbMJ3MbAdMGCxmZ1HLOivAdZl2AfJU0PV3s8qK6Y2Pnm6sBI8GO0Hj+NVNenWuxnsvcPhEP94y5UaycuklmnwfwR4xMz2Aj3AJ4NfAfvM7HFiE7m9wK3uHgEws9uAJ4mVej7i7vsy7IPkSLaraUYqtfa+alohzR3dSemXwZc6ONrZzW0bd7EzqKxxd9y9T55+oLp+jeRlsrORTnjlQm1trdfV1eW6G5JkLKppRnMxib+maloh6x5+YcT9iUad5s5ubg8uAqOpzReZqMys3t1r0x3Tqp4yKqPdxWkg8YtJ6iqVQ4mPwFtOnB71rlIhM3amea2qbuRspuAvo5LtpX+HczEZrDwzk/5oGWPJR1rbR0ZloPLF3t4oDc2dnD+7hFBo+GOLoXLsQ6WZMimnVCmm5CMFfxm11EnP3t4oS7+6hY5TvZROKWDXn15LQUH6C0DqRWKoADycVTUzmYTVBK7kG6V9ZNRS0zANzZ10nOoFoONULw3NnWlfF79IXPftn3PZl7fQ2xsFBs+xKzUjkl0a+cuopEvDnD+7hNIpBYmR//mzS9K+Nt1F4sK5ZYN+nlIzItml4C+jMlAaZtefXptI54D1qbuPG+5FIpVSMyLZo+A/ieWyDn2wG6wunFs26ARtKBTqc5EYycSwiGSHgv8kNZqbrLJ5sUhOw6S7wWqoCdqCgtCQqR4RGTsack1SI73JarQ3UQ1msBusNEErMrFp5D9JDacuPnmUn+kG5CPtiyZoRSY2Bf8JLDWApz5PTrsc7ex7XmpKaKQXi5EYKNBrglZk4lLwn6BSA/ijN67g49/vv3BZ5dTCPmvLD5ZvH2wD8kwXaVOgF5lclPOfoFIDeENzZ7+AHo06N2zYys6mNiIOdftbBs23D3QTVbYXaRORiU8j/wkqNU1z/uyStBuD7znYnnjNZQsqRpVvHyolJCJnH63nP4ENlfN3d9Zs2EZdYytL5pfzo1tWjbpmXmvXi5x9BlvPP6ORv5n9PXBB8LQCaHP3y4NjdwE3AhHgc+7+ZNB+HfBtYjt5Pezu92TSh7NZah499Xk2K2qUsxfJLxkFf3f/rfhjM/troD14fBGx/XkvJraH71PBHr8A3wGuBQ4CO8xss7u/lEk/8pmCtoiMRlZy/hYbcn4MuCpoWg1scvdu4E0zawCWB8ca3P2N4HWbgnMV/EVExlG2qn3eA7zt7q8Hz+cBB5KOHwzaBmrvx8zWm1mdmdU1NzdnqZsiIgLDGPmb2VPAnDSH7nb3nwSP1wIbs9kxd98AbIDYhG8231tEJN8NGfzd/ZrBjptZAfAbwLKk5kPAgqTn84M2BmkXEZFxko20zzXAK+5+MKltM7DGzIrN7DxgMbAd2AEsNrPzzKyI2KTw5iz0QURERiAbE75rSEn5uPs+M3uc2ERuL3Cru0cAzOw24ElipZ6PuPu+LPRBRERGYFLc5GVmzUBjSvMM4GgOujNWzqbvo+8yMZ1N3wXOru8zVt9lobvPTHdgUgT/dMysbqA71yajs+n76LtMTGfTd4Gz6/vk4rtoYTcRkTyk4C8ikocmc/DfkOsOZNnZ9H30XSams+m7wNn1fcb9u0zanL+IiIzeZB75i4jIKCn4i4jkoUkd/M3sK2a2x8xeNLOfmtm5ue7TaJnZX5rZK8H3+bGZVeS6T5kwsxvMbJ+ZRc1sUpbjmdl1ZvaqmTWY2Z257s9omdkjZnbEzPbmui+ZMrMFZvYzM3sp+O/r93Pdp0yY2RQz225mu4Pv86Vx++zJnPM3szJ3Px48/hxwkbvfkuNujYqZvR94xt17zeybAO5+R467NWpm9i4gCnwP+BN3n1RbsZlZGHiNpL0ngLWTce8JM/tVoBP4gbtfkuv+ZMLM5gJz3X2nmZUC9cBHJ+P/LpBYDn+6u3eaWSHwLPD77r5trD97Uo/844E/MB2YtFcyd/+pu/cGT7cRW/Ru0nL3l9391Vz3IwPLCfaecPceIL73xKTj7v8JtOS6H9ng7m+5+87gcQfwMgMsCz8ZeExn8LQw+BuXODapgz+AmX3NzA4AHwf+PNf9yZJPA/8v153Ic8Pee0Jyw8wWAUuBF3Lbk8yYWdjMXgSOAFvcfVy+z4QP/mb2lJntTfO3GsDd73b3BcCjwG257e3ghvouwTl3E1sM79Hc9XR4hvN9RMaCmZUAPwL+ICUDMOm4eyTY+3w+sNzMxiU1l5VtHMfSUPsJJHkUeAL44hh2JyPD2Bvhd4FfB672STAZM4L/bSajwfakkBwKcuM/Ah5193/KdX+yxd3bzOxnwHXAmE/OT/iR/2DMbHHS09XAK7nqS6bM7Drg88BH3P1Ervsj2ntiIgomSL8PvOzu/yvX/cmUmc2MV/aZ2VRiBQbjEscme7XPj4ALiFWVNAK3uPukHJ0Fm9wXA8eCpm2TtXIJwMyuB+4DZgJtwIvu/oHc9mpkzOxDwLc4s/fE13LcpVExs43Ae4ktG/w28EV3/35OOzVKZvYrwM+BXxD7/z3AF9z9idz1avTMbAnwt8T+GwsBj7v7l8flsydz8BcRkdGZ1GkfEREZHQV/EZE8pOAvIpKHFPxFRPKQgr+ISB5S8BcRyUMK/iIieej/A3vohW8MnHwtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejmJDxw5u0je",
        "outputId": "b88fcb92-8e94-4125-9dbc-79feb26416d9"
      },
      "source": [
        "learner.fit(X,Y_out)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "872vVREgu6fJ"
      },
      "source": [
        "pred_out = learner.predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "W3O2acyou-A3",
        "outputId": "13f6a02f-3d3c-4657-f36a-841b25d0117a"
      },
      "source": [
        "plt.scatter(X,Y_out,s=5,label = \"actual\")\n",
        "plt.scatter(X,pred_out,s=5,label = \"prediction with outliers\")\n",
        "plt.scatter(X,pred,s=5,c=\"k\",label = \"prediction without outlier\") # c-colors\n",
        "plt.legend()\n",
        "plt.title(\"Linear Regression\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8m87qq-vdyn",
        "outputId": "f05643a1-2f88-4242-afa0-32d91f742594"
      },
      "source": [
        "learner.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([45.30691889])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dNDKkPPvilL",
        "outputId": "2945c83e-9838-4578-de39-1ed2a2cf6696"
      },
      "source": [
        "ridge = Ridge(alpha=1000)\n",
        "ridge.fit(X,Y_out)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=1000, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Afa0IRzOvyYA"
      },
      "source": [
        "pred_ridge = ridge.predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "QwMU0KO2v3TD",
        "outputId": "be469a27-4fc3-4067-e495-6f745e4c0a00"
      },
      "source": [
        "plt.scatter(X,Y_out,label = \"actual\")\n",
        "plt.scatter(X,pred_out,s=5,c = \"r\",label = \"Linear Regression with outliers\")\n",
        "plt.scatter(X,pred_ridge,s=5,c=\"k\",label = \"RidgeRegression with outliers\")\n",
        "plt.legend()\n",
        "plt.title(\"Linear Regression\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Mxt6el4wUV3",
        "outputId": "3269bef1-31a4-4371-c057-975eb8ed5b8e"
      },
      "source": [
        "ridge.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22.32499265])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjT4b5UcwyS1"
      },
      "source": [
        " **Effects of alpha using Ridge on \n",
        "Coeficients**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYz97e87we8X"
      },
      "source": [
        "# generate data\n",
        "X,y,w = make_regression(n_samples=10,n_features=10,coef = True,random_state=1,bias = 3.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkcFRtnG0o_m",
        "outputId": "9bb4fa3f-adf3-40ac-9276-8add8ab246fa"
      },
      "source": [
        "w # weights"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([80.71051956, 10.74941291, 38.78606441, 13.64552257,  5.99176895,\n",
              "       86.35418546, 12.13434557,  4.45518785, 74.71216427, 55.6240234 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_BmfvNR0peK",
        "outputId": "3ca7b061-7cb2-42db-9a3c-5d7ec2ebd238"
      },
      "source": [
        "# generate 20 alphas\n",
        "alphas = np.logspace(-6,6,200)\n",
        "alphas[:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.00000000e-06, 1.14895100e-06, 1.32008840e-06, 1.51671689e-06,\n",
              "       1.74263339e-06, 2.00220037e-06, 2.30043012e-06, 2.64308149e-06,\n",
              "       3.03677112e-06, 3.48910121e-06, 4.00880633e-06, 4.60592204e-06,\n",
              "       5.29197874e-06, 6.08022426e-06, 6.98587975e-06, 8.02643352e-06,\n",
              "       9.22197882e-06, 1.05956018e-05, 1.21738273e-05, 1.39871310e-05])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VeroXNp08r2"
      },
      "source": [
        "coefs = []\n",
        "for a in alphas:\n",
        "  ridge = Ridge(alpha = a,fit_intercept=False)\n",
        "  ridge.fit(X,y)\n",
        "  coefs.append(ridge.coef_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvQ26YDLOJKd"
      },
      "source": [
        "Plot alphas and coefs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wkfq9Ijw1nfi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "8ceee264-9831-4e0b-933f-431e05d31cc8"
      },
      "source": [
        "ax = plt.gca()\n",
        "ax.plot(alphas,coefs)\n",
        "ax.set_xscale(\"log\") # e.g original data is 1.0e6 then it is transformed into 10^6 at axis\n",
        "plt.xlabel(\"alpha\")\n",
        "plt.ylabel(\"weights\")\n",
        "plt.title(\"Ridge coefficients as a function of the regularization\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2W5XA4qmOljH"
      },
      "source": [
        "Conlusion\n",
        "\n",
        "1.   When alpha is quite small,which means regularization is not too strong,weights are big.In other words,they're almost the same as they're randomly sampled.\n",
        "2.   However,when it comes to big alphas,weights are more likely to head towards zero.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClRptvzkQES5"
      },
      "source": [
        "Lasso\n",
        "\n",
        "\n",
        "\n",
        "* Linear model that predict's sparse coefs\n",
        "* Reduces the regressors predicting target\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUtnPSXaOgtg",
        "outputId": "fa31431a-1377-4274-f450-8c57a97ac863"
      },
      "source": [
        "lasso = Lasso(alpha=.1)\n",
        "lasso.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyDCIeBMQfTp",
        "outputId": "5a48805a-57d4-414f-8b38-10b15db9da5b"
      },
      "source": [
        "lasso.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5, 0. ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaEoLBrzVqza"
      },
      "source": [
        "Elastic Net\n",
        "\n",
        "\n",
        "*   Elastic Net can circumvent the problem that features aren't independent.While Lasso choose 1,ignoring the relationship between them,Elastic Net takes both.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TANhtCA6QijU"
      },
      "source": [
        "en = ElasticNet(alpha = .1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzYPsBgpW1QM",
        "outputId": "80911129-96f0-4f66-f9dc-4dca2a5225f8"
      },
      "source": [
        "en.fit([[0, 0], [0, 0], [1, 1]],  [0, .1, 1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
              "           max_iter=1000, normalize=False, positive=False, precompute=False,\n",
              "           random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPhRUtBJW7uI",
        "outputId": "6a8997cb-c0e4-48ae-d6c4-ff1b5929f4a1"
      },
      "source": [
        "en.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.32589556, 0.32579954])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0_Y9aSGX8Az"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrYIKP5nXBK8"
      },
      "source": [
        "# centers-number of piles of data(i.e. categories)\n",
        "# cluster_std- every data's variance \n",
        "# e.g cluter_std = [1.0,3.0](We want second data to have bigger variance)\n",
        "X,y = make_blobs(n_features=2, n_samples=1000, cluster_std=3,centers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KJFer8eDYGn_",
        "outputId": "52939f6a-2a2e-4780-a18a-a6ebd7ba07c0"
      },
      "source": [
        "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeZxN5RvAv++56yyGYexj3/ddiBCJbFkqaxGKokQ/UVKyZF9CUiJZI7KTfc++72OGYRjD7Nvdz/n9cccZtxlbjcZyvp+Pcu857/s+58x4znueVSiKgoaGhobGs4mU2QJoaGhoaDw+NCWvoaGh8QyjKXkNDQ2NZxhNyWtoaGg8w2hKXkNDQ+MZRp/ZAtxNQECAUrhw4cwWQ0NDQ+Op4siRI5GKouRM79gTpeQLFy7M4cOHM1sMDQ0NjacKIUTovY5p5hoNDQ2NZxhNyWtoaGg8w2hKXkNDQ+MZRlPyGhoaGs8wmpLX0NDQeIbRlLyGhobGM4ym5DU0/mNcLhfTP/qZrsU+ZELP73HYHZktksYzzBMVJ6+h8Tzw55ztbJyzDVuyneibsQQWz0uHwW0yWyyNZxRNyWs8dm5eucX2xXvJWSAHL3eqiyQ93y+QN6/cwmaxA2C32LkREpHJEmk8y2hKXuOxEns7jj5VB5GcYMFgMnDpaAi9J3XLbLEylcZd67Nq+kYQILtkWrz/SmaLpPEMoyl5jcfKxcMhyLKM7JKxJdvYveLAc6/kC5bOz5zzUwk6EkLRSoXIVSAgs0XSeIbRlLzGY6VwuUBklwyA0Wyg3IulM1miJ4Mcef3J0aJaZouh8RygKXmNx0qugjkZu3kYK79bT56iuej8RbvMFklD47lCU/Iaj52ytUpStlbJzBZDQ+O55PkOc9DQ0NB4xskQJS+EmCOEuCWEOH3Xd18LIa4LIY6n/HktI9bS0NDQ0Hh4Mmon/wvQNJ3vJyuKUjnlz/oMWktDQ0ND4yHJECWvKMouIDoj5tLQ0NDQyDget02+rxDiZIo5xz+9E4QQ7wkhDgshDt++ffsxi6OhoaHxfPE4lfxMoBhQGQgHJqZ3kqIoPyqKUl1RlOo5c6bbh1bjOSI5wYLL6cpsMTQ0nhkem5JXFCVCURSXoigy8BNQ83GtpfH0oygK33b5jrY5utMmezdO7zmX2SJpaDwTPDYlL4TIe9fHNsDpe52roXFm3wX2rT6Ey+nCkmhl8vs/ZrZIGhrPBBmSDCWEWAw0AAKEEGHAV0ADIURlQAGuAO9nxFoazzCKkvp3kXliaGg8S2SIklcUpWM6X/+cEXNrPB+Uq1OKum1fYNuiPZi9TQz4sXdmi6Sh8UwglLt3T5lM9erVlcOHD2e2GBqZiDXZhsGkR6fTZbYoGhpPDUKII4qiVE/vmFa7RuOJwuxtymwRNDSeKbTaNRoaGhrPMJqS19DQ0HiG0ZS8hoaGxjOMpuQ1NDQ0nmE0Ja+hoaHxDKNF12g8FzgdTtb8sInIsCia9WhEYMl8mS2ShsZ/gqbkNf4TkuKT+WXYb0SGRfLm/16nzAsl/vFchzed4NbVSGq3rIZ/7mwoisKW+bu4cCiIeu1rU6l+uTRjJr83ix1L9+GwOlj34xbmBU0ja4Dfv7kkDY2nAk3JP8XcCL7JmK7TSIhOoNe4rtRpVSOzRbonoztN5djWkzhsTg7/eYJfLk4jR950q0/fl8Vj/mDRqOUoisLcoYv4+ewUtizYxZzPF2NLtrFx7nYmbh9OqRrFPcYd3nQCu8Wufg45GUqVlyv86+vS0HjS0WzyTzH96w7l3P6LhF0MZ8QbE4m+GZPZIt2Ti4eDcdicAEg6ibCLN/7RPBtmb8WaZMOWbMdudXB230UOrj+GLdkGgOySObP3QppxlRqUw2g2IgQoskLh8gX/+cVoaDxFaEr+KcWSaCEmIk797HS4uB0WlYkS3Z967Wth9jFhMOoxmg0Ur1z4H81TtFIhDEb3C6jL6SJ/iTzUal4VU0qmrCRJlKtbOs24T+d8QNdh7WnR51Wm7B2Jf66s//haNDSeJrTaNU8pliQrrbO+jSKn/vzWJS/EaDZmolT3RpZldizZS/TNWBp2rPuPTDXgtu3PGjiP60E3efN/rXiheTW3TX7BLi4eDqZu2xewJtk4fyCIaq9UpHzdMhl8JRoaTx73q12jKfmnmFUzNvJ9/7kAfDSjF83fa5zJEmU+O5fuY1z3GdgtdiSdxLDfB/Jia61fjcazjVagLBOICL1NeEgEpWoUw8vX67Gs0frDprzWqxFCCPQG7UcJsGflAdXBKrtkxnefQZ2oGgihFajXeD7RbPKPgYMbjtGjXH++ajOOnuUHkBCT+NjWMhgNHgo+OcHCuG7T+bDmZ2xZsPOxrfukUvqFkh6fk+Mt2O6KqtHQeN7QlPxjYOHI37El20mOtxAflcD+NUf+s7WnfTibHb/t5eLhEKb0/omLR4L/0TyyLHPrWiR2myODJXy8tOnXjBz5/RGSQNJLlKhaRCtfrPFco73jPwZyFgxAfzgYp8MFQI586TsZkxMs6PQSJq+MU0KXT11NDVWUBGEXwylZrdgjzZGcYKF/3aFcDwrH5G1i8u4RFCoTmGEyPk4kSeLnM1PYMHsrQghe69Uos0XS0MhUtJ38Y6DftB5UeKks2fP6035gK6o0Spt088uwJbQN6E4b/24ZalZp3bcpJm8TZh8zBrOBqo0fPeFn26I93Lh0E7vVQWJMIr98uSTD5Psv8PHzpv2AlrT7pMVj84c8LMe3n+abNycy7+vfcNifrrcijWcDbSf/GMga4Me4zcPueTwmIpal41bhcrhwAVN6/0TjLvUzZO1mPRpRqGwg1y/dpPqrlcmW89HjwQ0mPaQ4KoUk3TMsU1EUZFnWWvXdg9BzYQxtOQZbso0DXkZib8Xz8fe9MlssjecMbSefGQgBdwV7CCljIz/K1i7FK13r3zPhR5ZlNs7dzs9DFnL5VGia4y93qkvF+mURkiBv0dz0HNM5zTmH/jxOK7+uNPfqxMJRyzNU/meFkBOhSDr3z9ZusXNmz/lMlkjjeUTbyWcC/rmy0vWrN5k37DckvcT/5n7wn64/d+hi/vhuA7ZkGyunb2D26cnkLpRTPW4wGhi97nNkWUaS0t8HjH17GtYkdymBRaOW80rXl8hVMGe65z4qsixzaMMxrEk2areq/sQmeD2Ici+WQiDQ6SQMZgP136yd2SJpPIdoSj6T6Di4De0HtEDSSf+5ueOv1YfVWi+SJHFu/0UPJX+Heyl4AFeKU1n97JQfSYbLp68yvvsMLIlWPpjcjRpNq6jHJvX6gZ3L/gKgQKl8TNs/+qk0CeUqEMCMQ2PYvfwA+Yrn4aX2tTJbJI3nEM1ck4kYjIZMUV7VXqmIycu9O3Y5XYScDGXb4j3I8sMr6o9m9sJgMqA36mnWsxF5i+a+7/n71x5h4cjlXDp+GYDPXxtN0JEQwi7cYHi7CSTGJqnnbl2wG2uiFWuilavnwrh5+dY/uMong8CS+eg4pA3136itJWRpZAraTv455L3xb5OzQAAhJ0P5a80hlk1cg96g4+xfF+j7XY+HmqPhWy9Sq3lVHDYnfjmy3Pfc9bO38H3/X3BY7Swe8wfT9o8m9lZqcTUFSIhJxDebDwC5CwUQHhKBLCtIkoR/7mz/+FrvYEm0EB+VSK6CAQghUBSFBSN+Z+8fB6ncqDy9xnRBp3/63hY0NB6EtpN/DtHpdbQf0JJXuzdEdik47U6sSTZ2pZhIwG0XH//uDFr4dqZPtUFEhactY+zl63VfBR9yMpS9Kw+ydeFubMk2ZFlBdskc33aa1/s2xexjwsvXTLnaJclTOJc67ts/h1KtSSXKvViKbzcOxTvLvwuDPLX7HG/m7cW7ZT5mQP1hOB1OtizYxdLxqwg+cYW1P2xixZR1/2oNDY0nFW0n/xwTWDIfssttojGY9JS4K2lqz4oD7Fr2F7ZkO8EnLvNlqzEMnN2HYpUKpztXUnwy5/YHkbdoLvIXz8u2xbuZ1OsHdDodiqJg9DKmFA0TlKxejNf7NeOlN+pgTbKSxd+Xk7vOUq5OKfQGPXmL5Gb0+i8y7Dp/GPiL6iQOPn6FgxuOEXYxHGuKX8KWbOfKmWsZtp6GxpOEpuQzmPCQCCb1+oGEmER6julC9SaVMluke5Ijrz9jNw9j6bhV5AzMTreRHdVjSXHJ3KlQqsgQdCSEvi8M4eOZvWja/WWsyTbCg2+Sp0guHHYn71UciCXRisvp4ovFn7Bswhpsye6aMV6+Zuq2qYnT7qRx1/qUq1OK5AQLu5fv58T201w+cw29Xkfh8gWYvGtEhptNTN5md7MQxW0aMnubaPBmbVZMWYsQAlmWadbj5QxdU0PjSUErNZzB9Cz/CVfPX0eRFUzeRhZemUnWAD82zNnK2h82U7xKEfpM7vbE11NJik+mR9n+RN3wNNMUKJ2fsZu+5IPqn2Gz2NAb9HQY/Drzhy9Td8tlapUkV6EA9v5xEKfdid6gI0uOLOQulJPPF35M3qK5GfTKN5zec04twQBg9jUzacdwSlQt+q9kj74Zw7GtpwksmZdSNYpz9fx1hjQdSeT1aF7t1oBPfuyNEILwkAjO/nWREtWKUrB0/n+1poZGZqKVGv4PibgaqTbyEEIQfTOWq+euM+OjudiSbVw5fRVJJ6mZj4qicGTzSRKiE6nVomqmpeG7nC5cTpcak+7j582w3wcysMFXOO2p4ZJ5i+Zm3U+biY9KQHbJCCE4vfucelxn0JG7cE4KlMzLMT8vZFnBmmAh5mYssbfiGPHWJL4/NJYLhy55KHhwlwbOmvPfNdeOCo+hV4UBOO1OZFmm/6z3adz5JRZemYmiKB4RLnmL5n5gVJCGxtNOhjhehRBzhBC3hBCn7/ouuxBisxAiKOX//6wV0FNGi/dfUR2KBUrlo2Dp/B79TO1WByEnU7NMfxw0n+HtxjPpvR/4sOaQTKlvcujP47TO9jYts3Rl1qfz1O/L1ipFuRdTW+kJSdDqgyb4ZvNBb3CbVPRGHUUqFqLT523JVTCAKi9XwOhlYPGYlcRHJmBPsiF07l8zRVYIuxjOX2sOU6NpZUxeRiS9hE6vI1eBAAbO7k2uAgH/6loO/3kch82BJdGKLdnOqukbU+XXQhg1nkMyaif/CzAd+PWu7wYDWxVFGSOEGJzy+bMMWu+J5b1xXanVohpJsclUf7USOr2OGk0rYzDqU+zCCq0+eFU9/05jaoDIsCgun7r6yFUj/y0T3v1etZ+vmbmJ13o1pkApt/nCaU/dbRvNRqKux9Cqz6sc33aa49tPkzWnH7+NW0W2AD9Grh3C75PWsHX+blxO9+7fbnOQLacfVp2ENcmGNdHK6E5TePubt6jUoDwJ0Qk06daQgHzZM+Ra8hbNrVb/1Bv1FClf4F/NF3s7juR4C3mL5tYeEhpPJRmi5BVF2SWEKPy3r1sDDVL+Pg/YwXOg5IUQVKpfzuO7gPw5+On0JI5tOUWB0vkoXbOEeixf8TwEH7+C7JKRZYWcgTkeeU2bxcbyyeuIi4yn9YdNyVcszyONv7tPLLgdlHd4tXtDLh1zyydJgiqNK2A0Gxm5ZgiXjl2mf70vcTlcRIXHMPadaYRdCFcVPIDBZGDQvL4snbCaU7vO4nLK7nDNpX8x7a/Rj3ytu1ccYFLPmSDgf3M+pE7rGh7Hty/eo/5dAF2GvQGA0+Hk1O5zePt5U6r6wz1Ety7anbKWoPqrlfh6+f80Ra/x1PE4bfK5FUUJT/n7TSBd46cQ4j3gPYCCBQs+RnEyl4B82Xnl7bSVJr9ZOYipfX4i9nY83Ud2/EeJPyPemszRLSdx2pxsnreD+Ze/x8fP+6HHD5zdm2/enITL4aJpj5c9nJDN3m1EnsK5CD0bRs1mVchbJPXHaE2yehRXsybZyF8iD9cu3EB2yRhMBsZvHcbPQxZx/mCQWvrAaDZQvm6qGehhcdgdjOkyFbvVbdIa1XEyq+Pne0Tj7Ft9WH37MJgNXA8KJyB/dgY1/oZLxy4jywrtB7ag2/AOaebf88cBxr0zHUVR+PiH95g18Fd1raObTxJ84grFKxd5oJyKonDzyi1MXkay53kurJQaTzD/ieNVURRFCJFuGI+iKD8CP4I7uua/kOdJIiB/DkasHvyv5ji16yyOFGXkcslcD3q0RiEvNK/GyphfcNqd6Tp+q7xcgSovp61LX6Z2SSrWK8PxHWcA+HDquxSrXJjv+88lOd7Cu6M7UbxKYU7vOa+GY7rt+k3pMbrTI1+n0+HyqJHjcsq4nC4PJV+2dkkOrDuKw+ZAdikULBPItQs3uHgkRK3Xs3zS2jRK3uVyMarTFJwpzuDx3aZ7+AdkWcHL1/xAGRVFYXz3Gexcug9Fgd6T3qFVn1cfOE5D43HxOJV8hBAir6Io4UKIvMDTW4DkCadi/XIc3XwCh92JTq8jsGS+hx57eNMJNv+6gxJVi9Lm49ceaV2dTsfItUM4t/8i0/r+zPjuM2j1wat89funHucVKJ2f60E3kGWFrDn96PFtJ2wWOyEnQ8lfIu8D3zr2rTrEyd3nqNmsCm982pIVU9ahKPDGpy0xmo1E3ohm3le/4XK4eGf4WwSWzMetq5G07d+cHHn90eml1IeMgOx50r4tyS7Zw/8guxTaftKc38auIjE2ma7D2pO/eN4H3pOI0NvsXLpPfQP4adB8TclrZCoZFiefYpNfqyhK+ZTP44Gouxyv2RVFGXS/OZ6FOPnMwGaxsXLaBuIi42nZ+9WHDgu8eCSYAS8Nw2axY/I28canrXjn6zfTnGe32okIvU2uggHptioc0mwUR7ecRHbJmH1MjF7/BRXqlVGPx0TEMv+bZTisDjoNbQfAB9U+w5psQ5IE47Z8hW82b/IUyZVm/u1L9jKx50xsyTZMXkZGrh1CvmLu67tT2rhbqX6Eh9xCdsl4+Zr57caPad5I9q0+xKxPf8U3mzeD5vVLt53hG7l7EHs7Xv0ckD87C658/0hF5GIiYulc+AMcKb1xs+b04/eInz3OOb33POf2B1GpQdn/3Mmu8Wzy2OPkhRCLcTtZA4QQYcBXwBhgqRCiBxAKpNUeTzmXjl1mXPcZ2C12+k7r8Y+yW0/tPsfWhbsoUrEQLXs3uW9533th8jLx1qDXH3nchUPB3HnE25JtnNh+Gv6m5G9dvU2faoOwWux4+5qZfmBMmrLEMRGxankERVE8io8B+OfOxkczUjsizfrfrx5VJ/vXG4qXjxmzj4npB8d4mEk2zduhmllsFjvHt5+mcsPy6vEV363jetBN9bMl0cqINyczev3nHjLUaVWDOq3cTtqTO8+wP+QmUTdiiQ6P4eVOdclfPC/dRnZgyvs/qmPiohKIuhHzSGGd/rmz0WfyO8z6dD4up4vkeAv96w1lxOrBZPH35eCGY3zTfgIulwudTse4LcMoW7uUxxx2m4Po8BgC8mdHb9BSWTT+HRkVXdPxHoee6S7Kn782ipgIt0L7uu04lt2c/UjJTJdPhTKk2Sj3LtXbRNzteN7+6r97FlasXxYppUuV2dtEvXae9c5dLhcD6n9FfFQiAPZkO/O+/o3CZQuQv0Re6rSugRCC7iM6MOz1ccguGbvFwV9rDnvM9deaw1w8EkzNZlUp80IJvHz+9jaguJWzzWJnzcw/6THa3Ylqw89bObbtlHqa0cvo8YYQFhTOz0MWpbmuI5tPsHrmn7R4/xWPh6aiKPSrNYQLh4LdX6Q06Fo+eS1zL3xH816vsHj0H9y+Fqna4LPdo7vW/WjZ+1UkSWLmJ79gs9i5cCiY+cOX0WdyN+Z+uRibxR2u6sTF/nVHPJR8ROht+tUaQnKChaw5/Zh+YMw9O3xpaDwMWhXKf4iiKMRFJqR+lhUSY5MfaY5zBy6pf7cl2zj85/EMkevkrrMc2XzCI5QxPQqVCWTy7hF0HfYG/5v7Ia/3a6bOcSP4Jid3nU1TfXL74r3M+WIRIztMZmJPdxZp+bql1XaGiqKwdeFu7Fa3Ivtz3nZGdZzCgm9+59OGX7F65p+83LleuvLo9Dp8sqba5/+Ytl5tTqIz6Hi9b1OqvZL6tpQQnYikS/srLLtkfhjwC/O/WebxfdjFG6kKHkC5Ey6qEHQkBICp+0bxytv1adDhRabuGYnRZLjvPbwXibHJ6v13OZwkxCSyc+k+rpy+qp6jN+goUdXTXLN0/CribsdjS7YTdSOGdbM2/aP1NTTuoL0L/kOEELTu25QNs7cihKBi/bIE5H+0hJ5yL6bu4EzeJmq3qnGfsx+OKb1/ZNui3QghKFunFN9u+OK+sd3FqxSheJXUsEBZlhnefgKH/zyB4pI9gub1Rj16gw5rkhOQ2fTLdiJCb7szV3USckrki9nHhN7o/tXau+Kgam6xWx3M/OQXz0D8FIQkKF2zOK/3S3X+FipbgGvnb6j1b+q/WcdjTMnqRSlQOj9Bh4P/Ph0Om5NDG47zztdvpcpv0LsfRn9b3ml3UTglaSpHXn8+nfPhPe/Xw9KkWwNWTltPUnwykiTR4bPX2bF0n0eJiOJVilCv7Qse40zeRiS9DtnuRJIkTE94jSONJx9Nyf8L+kzqRsMOdbFb7ZSvW/qRE2UKlQlk4vav2bnsL4pUKEjjLi/9K3lkWWbDz1vV5KaTO9078fSySc/uv8j3H8/FYNLz8cz3KFzOreSCj1/h6OaT2FNMCl5ZvPDJaiBLdl/eHd2JbztPVedQFDi+/TQoqSUDJJ3EJ7PeV80klV8uz9Gtp1RFf3cEy90oisLtsCjW/biZ5u81xuxtpv/MXkiSIPRsGO0+aZHGSanT6eg94W0+bzZKNYGoxww6ares5vFd3qK5admnCWu+T90dC+FWyHfb3U/vOcfYt6fjdDj5aFpDar2SBMZaCINnktvdsu9ZcYCo8BhqNqvCX6sPI4Tgh2PjiQ6PIXfhXHhn8aJe21r8PnGNOqbHt2kbpHcY3Ibj289w6dhlStcsToveTdJdU0PjYdGqUD5jvJm3p+onMHkb+f3WnDQVL+1WO+1z98SSYEEI8M+Tjd+u/wS4TRq9q/xPVZo5CwTw9YpPKV6lCJIksWj0CuYOXXzP9YUQ1Hm9Bl8v/x/gVmZrftjEsgmruXU1UnXQ3g+9Uc+Mg2MoWrEQUeEx3Lh0k2KVC6fbPMTldDGw4VcEH7+Cw+7EP3c2/HP70fy9V3itZ+N0H7w/DV7AH9+tx2F1YPI20W96D17t1lCVt02ObiSlmN6EpPD9n8EULedCZJ+PMKZ1rv846FfWzNyUUuRNRqeXAEHh8gX4/tBYj3PDgsI5vec8paoXpUiFQve8B38vpqahcT+0KpT/EZHXo5j+0RwSohPpPrIj5V98+KxOp8PJN29M5OCGYxQqG8iYjUMfKfvVbrXz/SdzMft6Ie4KA7weFJ6m0UdCTBLOlEJoigIxN+PUaI/AkvnoPLQdC0YuR6eXiL0Vy8AGX1G1cUW+XvE/woNvepg8dAYdSkrHJwAEqp085lYcZm8jBpOemIhYNVY9PZOJx72wO+lXawh6gx67zY7RbMTsY+aHY+PTOCF1eh0Tdwwn5EQoWQOyqGGV96Pz0HYc2XSCaxduULlhORp3TX2DunL6KslxFvWzIsPgjoX47cRZsG1PV8lvXbhHrT8EqPci+PgVLElWvHxSk6gCS+QlsMSD4+01Ba+RUWiO1wxkaIsx/LX6MCd3nmVI01EkxCQ+9Ngt83dxdMspXA4XoWeuMefztFEj92POF4vYPG8n4cE3UWQFRVawJdv5fdKaNOdmz5PNww4vJEHP8gPoEPgeG37eSschbfkjai5OuwuHzd0a8PCmE9y8cgu/AD8MKfZ2o5eR5r0a0+2btyhZoxiSJMiRz5+eYzozttt0OhXsTftcPfh9oruBiCIrGMwGdfz9sFsdJCdYcNrdYYiJKY7L9NDpdJSoWvShFDzA2h82E3YxHLvFzrGtp5jQYybjuk3nrzWH+ajOF3i+3QoSYvTYrN73NNcUr1JE9UHcecjpdBI5C+R44vsGaDz7aDv5DCQsKNxjRxsZFkUWf9+HGmtNsqHI7rGySyY5wfKAEZ5cPnVVzbK8g8FkSLd2ihCCsrVLcW5/kLpe2AV3OeTp/X5m8diVhAff9IzZVxR8/Lzp9EVbLh4J5sLBS1RrUonek97BYDTQcUhbbBYbRrORK6evsvv3v1T7e0To7dT2f5KgVI0SnNx59t4Xk85OX9Ldu6F3coKFce9MI+joZRp1rkf3kR09dsKyLPPLl0s4sO4o1ZtW5tKxy6qPwGF3sm3RbmSnzLbFe5B0d+2gBZjMEqWrCcy5v0CYX+HWtUgWjPgdIQRdh7UnIH8OPl/4ET8NXsitq5E07d6QI5tPIgR0+bK9tiPXyHQ0JZ+BNO5aj60L9gAKuQrkoMAjdBtq3PUl/pi2nujwGPRGPV0fMV6+9YfNOLPvIkKA0+bE5GuiYOn8JMUmsv6nLTTt8bKH0s5VKEBVvHdjtzoIv5SaXOTr74PJbKTPlO745chC2MUbtOvfgnIvlkrzALuTrXp2f5BauhjcztvEmCQknYTeqKdQ2UDO7b/o2TTkLsWuN+g9HLRmHxOVGpSnditPR+odZg9eyMENx3DYnPzx3XpK1SjOi6/XVI9vnLOdFVPXY0u2cf3STWq1qIrZx5TyYFVUR7XL4cKV8pw0ehmo8nIFGrz1IvXfrI1kNLibkNQdStSNGIRwx+PPD56BT1Yf+s98T13vpfa17/Vj0tD4z9EcrxlI9M0YZg9ZhCQJeo7pxJb5u7l6LoymPRpRtlbJB453OpxEhN4mR77s/+g1/9Kxy1w5c43KDcuREJNEv1pDsCXb0Rv1VG5Yjv/N/VDd2TsdTib1+oEjm04QEJidK2fCkJ0utRY7uCNP3vi0Nb3GdgHgwLojjHhrEjq9DqPZyI8nJ6LIMr9+vRS71UGXL9uTr1ge3srXi+ibseo8pWsW5/xBd06A3qiny5dvcGrXGc4fvESRCgUJOnYZ2102bUknIcuyqvQlvYTJbKR41aJM3P61ujt2OpwEnwjlp8/mc2L7GXW8ycvIF0s+oXZLtx/qk5e+5PSe8+rxln2aULlheYKOXibkZChHN5/wuO5suWa7CvQAACAASURBVLLSum9TOg5u41H8LDE2ifa5e6ix+5JOYmXsPA+b+/OM4opAiR8OcjQiywCEseaDB2lkCJrj9T/AZrHxQfXPiLsdj86g4/Sec0SGRWOz2Nm2eC+zjo9/YIErvUGvnnPp2GX+nLeDwJJ5afH+K+h0OmRZ5ocB89i76iAV6pZhwE+91XZ94BnzfnD9MTUc3Wl3cnjTCboU/ZCqjSpQ7sXSNOpcj0G/9FXHXrtwnQUjl7Nt4W71O7OPmfYDWqifl4xdqe7QXU6Z/WsOs2ziGm5cuoksyxzaeIzF12Z5lB/W6SXsdic6g05VjgaTjjF/fklMRCzvVfrUQ8EbvYwE5MtO5I0o7Bb3tlp2ylgSrVw4GMTswQvwyepN0x6NGNp8NGEXw91x9MbU3b/NYmdkh8msjv+VsIvhXDiUmnQmhCD4xBV2LfuLOq1r8tXygczs/wsb52zD6XChN+qp07o6XYa2T3kQzuTI5pNUe6Ui/Wb0pFDZQK6dv4EQUKhcAU3B34US0xuc5wEXSkwvyLkdIWVMMxiNf46m5DOIaxdukJxgxelw74ZvXIpQHXg6nUTQkZCHqmIIcPPKLT556UusSTZM3kbCgyPoPfEdNv+6k/Wzt2JLtrE7Yj95i+bmneFvpTtHqZrF1fLDACjgsDo4sO4oB9cfY9mE1cw5N4VsOd3RKgVK5adp94ZsW7Rb3UH758nqYQfPXyIv5w9ewml3IgTkLJCDsAs31OtMjrcQeyuOT+d8yNdtx2NLtqEocPXMNXX3Lcsy84cvo8rLFRja4luPOjdGs4FOn7el3SctmN7vZw6sO0pyfLLqa7DbHCybuAYhBGtnbSYhJhFrovsBkatQAJFh0apPxOlwIrtkLAkWdHq9ahoymPUEHQnBYXOybfEeytctTbOejTi1+xwRoZGUq1OSXmO7ArBqxsaU7F0HG+duByGYvGuEmgDXrOfLD/XzfJZQ7CdQ4ocBMsJvOMJYNfWgKxS480YkwHUTNCWf6WjRNRlE3iK5kCSBEG6TREBgdjVbUZZlSr9Q4gEzpHLhULBqP7cl2zm08RgAt65FqtUN7VYHN6/cu3qzorgjWe51zOV0cf6usgrgzro1eaW+Gfy9Y1SPbztTpEJBsuXKSofBr1O9SWUqvlQGo5cRg8lA7sK58M+TjepNKrEiag5CpJTwdbjUFH/ZKeOwOtxtD5NtHvNXaVyBOq/XICYiltvXonA5XbzQohrl6pamaKVC7jIEKeGat69FIbtSa9TnDAyg3SfNMZoN7ofFkLYYjAZKVi9G+bqlMXkbMZoNFKtUWFX4LoeTo1tP0v/FoYSeDcNutXHrWpTbVITbYXznAaPICpt+2UHk9WjafdKCtv2bY/Yxc/X8dW5dvX3Pn8OzhKLIKDHvgvMcOC+gxPRAUe7aSHi1AeHt/iPlBn3xzBNWQ0XbyWcQPll9mLx7BL+NXYlfjix0GdaefasOE3bxBo061SVP4VwPPVepGsVURWPyNlKjaRUAGnd+ieWT1gIKLqdM7ZbV2bpwN6VfKJ7mLSF7nmzcL67D5ZQpWMbTMVy6ZnFqvlaVv1YfRtIJ8pfIS8ssXchTJDdGk54rZ64hywp6g44zey8AMGr95/w5dwcOm4NXuzdUy/IaTUYKlAnk+sVwFEXB5G3E6XDhsDowmA2UqlmcLQt2Ae5Y+5LVihIdHstHtT7HbnMgUmQ8tOEY/ab3xDebD1+1Gech75v/a8WqaRsJCMzOZ/P6krdoblp90JR1P25mzQ+bWD97Cx/N6MXo9Z9z7cINsvj7cDssik8bfu02KSmwc2lqFJDsUrh2/jp9qg5i2v7RvNazMSu/26C+qRhMesIu3KBg6fxcPX+dUR0mc/X8dYSAnmO70Paj5h7y2W0O1sz8k7jb8TR/75U01TufPuygpFYPRbGBYgXh3kyILEPB9BLIcWBqhBDGe8yj8V+iOV4fM4qicO5AEEK4a7M8bEjdHZt8gZL5aP5+Y1V5xtyK48KhYGSXi9GdpiJJAofdSa0WVWnzUXMqvlRWnePAuiNMfn8W0eGxSDqJ3IVyYvYxkiNfdt74tFW63Z4AbgTf5NvO33Hh0CXu9fshSYI1iQs8fAJ/59yBi/w6fBl+2X15Z/hbrJy2gVO7z9HgrTrkyJ+dcW+7W+0FlspLly/fYGrvH7EkWj3mSJNsBSDcHbUWhc5Mcz+XjP2Dnz9fpJqcDCY9v934SY0EigqP4dbV2zgdLmZ/toCzf11MV3a/gCzMPTeV5VPWppiIwMvHiznnppCcYKFXhQEeCVBmHxNrEhZ4zDG8/QQOrj+K0+HCN5sPvwZPf6S2jE8icuynYNvi/mB8Ccn/u8wVSAPQHK+Zyrh3prPnjwMANHjrRQbO7vNQ4/5eOAzgdlgU/2v0NTcuRRAQmF2N9QbYs+IghzYe57t9oylasRBHt55i6fjVRN+MVc0zN4JvojfquR0WzZIxfxBYMp/aOFyWZc4fCMLsY2bV9A0EHQ2+p4IHdwSK4T4VGs8fDGJQo28QOuG2jSdauXwyFIfDRdCREGYPXqieG3YhnD+mrvPIiJUkyR3mmGxLo+CzZPelbpuauJyuNPXWd/y2zyPGXpYVkuKSyeLvy9pZm/i+/y9IkuDFNi9QulYJLh2/kiaMFNzO6tN7z9N9REcqvlSWiCu3KV61CGPfnsbV89c92hAC6ZaYPrb1lGrucTqchF24QakaD2/C2LfqECunb6BwuQK8O7rTAyOuFDka4KGcnYoig2U5iisUYW6FMDw4+gtAZB0PjsPuVGDjvy+op/H40ZT8YyQ5wcL2JXtVe/SmeTvoO+3ddLsrPQyzBy8gPOQWiqIQExGH3qjzqGoIgvMHgvD192FY67EeD4E7OO1OEqITObbtNO9VHIhXFjP5iuXh4pFg7BY7OoOObDmzeigxSSfQGfRIkoROL1Gudik+mNrdYxftsDs4tescvv4+lKxWjD/nbvewue9fe0S18e9asT+NXBcOB9O0e0O2L95LzoIBfDavL7JLZvL7s7hy+lrqiQokRCWyYfZWzN6mNEW+KtQrw5XTV1X5y9YuqZpJfhw0X/Vp7P3jANMPjUGn03HxcDAN3qrNqhl/cvXcdWSXu3dsgVLuNop3yhv3rvoxl0/dQP5bBeesAYIx65qiKE6ESP0nVb5uaY5uOYXD7kCSJPLfVc5AURSU5CXgOAC6QmBZDooT/L5B8mpC8IkrjO48BVuynTN7z2NNsjLgp3tvEOTEWZDo3lUrvh8g+d6/kqaSMAGSFwIWlOQFELAeoXtw20ghhKbcMxjFfgQlaQ7o8iN8P0ZIPhk6v6bkHyNGswGD2YAr0a0VzN7uErz/tPiU3epQs2IlnUSFemW4fCqUuNsJ6oOk3IulCA+JSCmSdW/c9e+TSIxN4va1KPV7l1Mm6kYMBpMeWZbR6fV8f3gsdoud+KgEKtYvi8GYuoOPvB7F1fPXmT14IWEXbyC7FDoOfp0CpfNj8jaqIZd3O3EVV9o3BJ1OR/eRHRk4+wMSY5OY9/VS4m7H03FwG777cDZJcckYTHpcLheyU8FmsbPr97/SKPn3xncla0AWTu85zwstq9P6g1fVe+3lY8aSYE25ThdePibeG9dVHftyp5f4fsAvhF+6yZv/a02BUqk+C8V1m/Dga8iulLj5u5K3bMkucmYZhRL9O2RfgBDuc4b+NoBlE1YTcyuONv2a4Zst9R+vYlkKCWOAv2U2xw1AMR0k9Mw11flutzq4eDjk3j9LxQ6JU1AjWxJnoPi8ixD3aWBj23bX2hI4TsJDKPnHgaI43ZE4ulzPnR1fcYWjRL+L+2dhRHHdQPhPz9A1NCX/GNEb9IxcM5jJ789CEoI+k97h4zpfcPFICGVrleTbjV88UiepbiM6cHLnGaxJNnyyehOQ358eozsRejaMkJOh1H+jNoXKFiBXogVvP29cThkhoHXfZlR+uQILvllKyMmryC6Xu8rkPawxvv4+WBIsuBwyegMsm7iaoMMhZM3pR2DJfOrO+OSus3zRfDSAe9eeMt+yiWtYHjmH65dusnrGxntej6R316AXOkGvcV3URK2v247nzL4LOO1ODqw7wvyQGXj5mrl1NZIeZT/hjjK7dS2Kz5qMIPZWHC16v0LL3q9iMBro8uUb6a439NdyDO+4C0uSwGgWfNvlOybt/AZw1w769ZtlRFy+hcGkxzvL3+LfHadp1jmedb/6IQRYktyVJsFd5C32th0fv7PgDAKDuzCd2dtE12Hpy4L9EGkUvHs2wEbFBuXQ6SWMZgkhCZp0f/Ge99Eth47U8EUp5fN9MNWB5BuAFRQX6Mve//zHhCLHoES1B9dtd1ROjqUIfcFMkSVTcIaA0KX827G7H7YZjOZ4/ZckxCTyx9T1uFwu2nz0mhp3npxgYfL7s7h8MpSWfZrQ+sNmzBm6mGUTVuG0uzCY9HQd9gYdh7R9pPXsNgfj3pnGvtWHcVgdmH3MzDk3RbWt3yE+OoF9Kw/hnycbNZtVUXez1y+F816lT3FY7SiKuzuR0+GWx2F3kqtgAPXfqMPKaevVUEMhCRRZQUiCohUKMXzlIHb9vp8t83cScjI0jYwFSudnztkpJMYm0S6gO7Kc9nfM5GWkSMVCePmaeXv4m5Svk1qxs3W2t0mOdytAryxeTNj2lVpL/oMan6ldnCSdhBBCfYvxzerNsOWfejiUD286wbxhS/DPJeg3YiM5cifisMOBLX6M+6gUs45P4PdJa1j30xaPNwy/gCwsvzVH/ay4wpFvN+PYLomEWDOHdpVi9yonAgsFSliYujYIa7IXG1Z+hqL40Py9xvhkvfdrt2zZBHGfAnbcSjpFMXu1Rso6AkVRiDjRjv0bIslfxE61Vwoi5Via7lyKHIeSMAksv7vn8RuF5N3qnmsDKIoDJelXcIYQdK46FmsJdzvIf9Bj+N+gJM1GSZgMOAAJvNogZf32P5UhM1HkWJTbTVKilvTg3QHJb8gjz6M5Xh8jAxt+xbXz7oSgHUv2MvfCd0iSxPf957L3jwM4bE5mD15IkQqFsCXb1Nhu2SW7KzMqClHhMXj5mj0iLyKvRzGx5w/E3IzhnW86qCn6RpOB8wcuqYlOkk4QcjI0jZL3y56Fpu++nMY0FHz8Cnq9Drua8JSN4SsHcfnkVUpULUKRCoW4dOyyugM3mPQosoJTdqHICuGXI+hd5X8kxiV5vgmkmC+EJMhdKABFUfDN5kOL3k1Y/f2fqadJgjyFcxEXFc/5A+4Cace2nmLK3pGUS+l1+kLzauxbdchtUvE1U7BMoDr+rUGvM777dCRJwmaxe7Q4TIxLZnj7CayInMusgfPYtfwA0eHRyC4FSSe4EZSfzp/cpEq9RPIWcivXpNgk1s3aksbJ/HdnrNDlRcqxgKqvLgZdAer37M7LW85jibtMjdq/IOnzM+itUlw5uwYU2PTrTn46OVG996Hnwpja5yfsFjt9JnejXJ0mKNJMcBwHY22QcgBO0BV1L6gkkCvPeVp1T6nh44hFUSxpTDCKnIwS2RrkGNxKossDFTyAEAaEbw9GdZzM/nUrAKjVvCpfLP7kgWMzFiOp6ToS8HRHHz0qQsoGAavBug6kPGB+7cGDHhEtGepfYLc5uHLqGk67E5fDxa1rUSREu8sLXz0X5lGAKzwkgvYDWpItV1ZM3kay5/Wn5QevMrLDZN4u1pc38/Zi3+pD6vnD203g6JaTBJ8IZVSHydy6Fqkeq9m8KiZvo1uBKFCiqmcUDrgdexN6fM+rhrfoUOB9Qs+6nZdFKxbClRKtYvQyUKNpFUpUKUqTdxqoTSyKVylCvxk90Rvcr/yyomD2NWH2MVGtSSUcDsc9TT2KrHBy1zlCz1zj7F8XaD+wFb7+7h2t3qCjZPVizAuapu7U77Bo1Ar175/N68uHU7vTfUQHfjg6ziOqpP4btZmyeyT9Z71Pv+k9MHp52nCtSTa6Fv2QFVPXExkWdddDVSH0golJAwvwfsNSbFxahm9WDmL/uqPpRhG1H9AyzXfCUB4p6ygk395IkonqTSpR743XMQeuxJFlI8EnonHYnDjsTq5fDPcoNf1ZkxGc3n2WC4cuMaTpSGwWGxhfQBFZUKx/gmJD6IulPpCFb0q2qOT+I+UG0imh4DwLShxu048FrCvSnnMPLIkWdi/fjzXRijXRyu7l+7EkPlr103+N95tgrApIoC+OyPLvWy8+bQhdHoRPD4RX88dStVTbyf8LjCYDhcoFpqT2Q0D+7GTJ7o7Hbte/BePfnYGkkzCYDNRsVgX/3NlYcHkG0eGx5MjnT8jJUA6uP6pGfEzv+zN1Uvq8hofcUkMHJb1EZFiU2qLuw6ndKVqhEBGht2nyTv10ywkf23qKncv+QpEVom9EM6X3j0zeNYLAkvkYtW4Ia2ZuomDpfHRIMReFBYVz5fRVytYuSfY8/uz546BH0a7qTSrT9uPm+AVkYffvaaNj7q4cKbtkPnt1JNYkK7JLZsDsPhzddALvrN50HtoOIQRFyhfk8qnUptZ+OVIrWur0Opr1aHTP+353eGmpGsWZOeAXzh0IQhICc4rtPn0E1iQdQvKiStOPyOofzqppS3ArUoGQ3HP3GN3Zo2H4w2A0G/HJ5qM+5P1y+KqOVlmWiQ6PUWsJuZwuEqITye47FSwrAavbERuwEaHLjSIngjAjsi9BSZwMSIgsn6SvAHSBbps6AHrQFUOx7UdJnA66nIgsQxG6HGnH4a4TZPI2qQ9ck7cpzUMTQLEfRYkfAUKP8BuBMDx8M5wHIYQZkX2e1gnrMaIp+X/JxB3D+X3iGlxOmfYDWqg2zfpv1iGwVD6uB4VTqUE5sgb4AWAwGlTHpdFs9NhF3h133rpvU5aOX4UQgoD8OTh/IIgpfX6kRJWi9J3egxbvv6KeGxcZz5wvFpEQnUTnoe0oVqkwdqtDzXhVFDx6oFaqX45K9VMbYBzffpqhLb9Fp9MhJMHMo+NIir0rsxF3gpSiKPR9IX17Yc8xnVMKmNmo3ao6e1YcUAuMbfl1J6PWfe5x/nf7R/Nmnl5YUurmH1h3FGuy7cGx4IoTJXkROM6CVxt2LrvE2X0XEZKg95RuzB604L7jAWSXu9/rtx2+JT5azx1bU51Wlfh88WCM94n/dzld/PnLDmJvxdG4Sz21UUnw8Sse9zh7Pn/1d0GSJBp1eYk9Kw4ggBLVipIjX3aU23uAO8lfAsV+GsU6EmxbQZgQ/nOQsk2877UIXR7wn4mSOAN0ucGnD0rUG4AFHHoUVwQiR/oNaHQ6d6G4qb1nAYKPf3hPTbq7g6LY3KUMFHc7RCWmO+Tcl+EKWVPwjw9Nyf9L/LJn4d1RndI9VqxS4TSt9+6mcLkCtP24OUvHr8Inqzefze+nHus67A0qNSjH7uX7SYhO5KfBC3Ha3Qk1Jh8T74/vitHsNtkMbTmGS0dDcDpcHNl8gkWhM6n+aiVKVCvK+QNBSDodH0zpfk85Vk7foIY66g06dv++nzK1SnJq9zn1nHzF8vDdBz9h/VtGKrgTo8JDIrAmWhFCcHLnOVXB6Q06sudN+6Zh9jKpLQjB3bzj1tVICj6gBr8SOwhs6wAFJXkFsaGFcTndzu6fP1vIW5+1Zu7QJemOFZLAL0cWek96B7vVQViwDu4q/lD39dIeCt7pcDLni8Wc3nOOhh3q0uaj15jcexY7luzFaXeyfPJa5gVNwzebD/HRieh0qdbPpLhkj7UHzf2QRp3qYbfaVUe4YnoRLKtwK3oZsIF9N+AExYkS/xUiYPV97weAML2IMLmjbxT7MRT1mpzgOH3fXXKZF0rww7EJ955cTnDH76ufY1JkfUD0jsYTg6bkM5l3R3VK08noDvvXHmbjz9s8koocNidb5u9k3Q+bCAjMweRd33D5VKhqWlFkhVvXoihSviATtn1N5PVosmT3ve8OObBkPoxmA3arA71RT+7CuVgydqXHOU6HS7XlgzuyRdJJKIpCvmK52Th3u1oyWFYUajWvytEtpyhasRC9xnVJd93y9cpwYvsZt1lKUchZ4CEqFtq2ccchICR4q184fy7JmvJZ4HLJSHqB7PS0sweWzMfAn3uz47d9HN18kkk9Z+Jy3u2SEswbvoPGb6eGPP769VL+mLoOp8PFxcPBJMYlsX/NkbvKLTsJORlKxZfKUqFeaQqUzs+189dxuWR6/O3BL4SgehNPE9Dt2A+YPywRlES6DX+N7H7K33wd/2B3ayjjriWjzuME60p38bB/gpQDjDXBftQtjqmJmgfwsCiK7C6FIMeC+VWElPXBg/4DFEUB+0FQYsFUHyGezbLRWgjlE8w7JfpyIzgizfd34sslSdCgQ11kl4v9a47gkmWy587GnPNT1R2p3equZw/wcscX0601Y7PY+O7Dnzm77wINOrxI56Ftae7V2aOcgBACbz8vEGCJt1Dt1Uqc3HnW4w3A5XShKO748AVXvldNVH/n4pFgvm47nthbcbicsrqOwWxg3KYvKV+3jHpuYmwSt65GkhSfzJJv/8Avy3F6DT2NLMOPw/Nx/bIvF48bkHQSny/6mIk9Z6oJT3cwmA18u+ELJvX6gYgrt9wPq3v82m+0L0Gn12Gz2GiXs4dH1vCdQmqXjl3GYXOiN+hQFPDyNTN85SDK1SlF8IkrZMuVVfWfKI6z7vBG4YPwG+I2r+BWMJ0L9yHqRjSfTb9C7SbxmLwM7sqNznMo+EC2n9GZK6cv6H2QY4eAdXnqF949kPw+e+R57qAoTrDvA/RgrP3IphU57qsU3wOgy44I2PBEKFQ5fhxYFoIiQF8AkWMFQtzbVPcko4VQZhIup4tz+y+SJbsvhcoWeOTx5euWIfJGjEc4n7tJtA7ZKaPgduoNnv8RWxfuJikumUad62FNsjK60xR3jRW7i6jwGAA2ztnK5F0j0vwjvX0tirYfv8anP/dBCEHouTByFcxBRGikmqmqKArWJBu/XPyOHPn82b38AMe2nr7rWmUKlMmPj58PPUZ38lDwibFJXDwSQoFS7lo5I9+a7JFleweH1cEXLceQxd+HgqXz0+6TFnzZaixOu0N1WiIg7FIFnI5kLp814XK5r0Wnl9i36hC5C2Yhf8EIYqN0nDviR42mlWn67suUrlmcmyER6cbsg/shFlgyr9oJ6uLhEPjbBsjlcFGqRnEq1i/HtQvXObThGE6bk8TYJEZ3nsqSa7PUeH5ICW+M7gJKIiChRB0Cv6/A1ITkBAvR4bHkDrRSq0kcJi93EhSuy1y8uoIvmk8gMfZbWn/YlD6Tuz1QsSq2/Shx/UGxg1cn3JE47jHC3PS+Yx+EEHp3dcl/inUtauKXHJuSMJZ+cbz/FMsSUFLkcl1LkStzksIeJ5qSf0y4nC4GNvyKkBOhyC6Z7qM60q5/iwcPvIuPZvZCb5DYv/YoMRGxKIp7NxlYIi/Xzl/HL0cW3h3ZEZ1eR5N3GqjjhrefwIG1RzyiYwDOHwjCkmjFO0tqrPWCkb+z5Ns/QAheaF6VwfP7MeClYcRHJaSxFiiKQs7AHDgdTiwJFqS7OkApikKlBuX5aHpPjzFR4TG8X/lTHDYHskvm2w1f3LdJeXJcMslxyURcuc2JHWfUyKPUheDCMRmj2Q+XK/Xh57A5ObD+KD6+CdwONzBw8jUkr/IUrpnqKC5WpQiXT4Yiy7K7t2tKMljlhgXo+slFilayoTgvI/RFyF0oII1sJm8jVRtXpHbL6oSeC+PIphOp61uTkCNSGmj4jQJjXZaM+o59K/NRtX48XT+9iY5It0/B+zDeWT6nSMWCxN+6jHT3fRY+TOjxk/v+Axt+3krjLi9QvOgYcJwCfQWE/wyElOWue29HiemBO6EISP4ZjNUBPWQZiDCUv+f9/k/Ql0jJ5HQCCugevvcxgKK4wBkMkj9Cl4HlmnX53Yod2V1wTXr4cuBPE5qSf0wEn7hC8PErajnahSOXP7KSn9B9BvvXHvEoGWAwGeg1tgulXyiBt59XmmgIgOtB4akKXoBw/4esAX6YfTxt8wtHLldDH3f9/hf71xzBYUtRnndtZIUkKFOrBAjoWKC3GiooJIHRbMDpcJEzMAcul8tDpp1L95Ecn6zmDHz+2miMZgN6gw6dQUfpmiWwWewEHQ1xv53ctXu2W/+m4FNQFHcS183Lnk1TLAkWkmJ13L7hxYSPCzD9zx0exxt2eJHLJ0NBCAwmPUUqFKR132Y0avoNuK4D53HcPEakfQUB+XPx5bKBzB++DFmRyV8sD/Xa1VKT0gqWzs9Lb9Rmx5K9CCH4cGRwyo4diBvA1hWBLB7rh9XixZULJrLmcNKmZyTuWPa14NWSaetDiL6ZSHBQU0pX2IOQvBHZpuKwzr3rxgsCc/YH+xX3Z8d+lPjxiGzucgyXjl9m1oBvMeoC6TPiOvkK2wEX2A8ABkicAf4z072PHvfUthvFuhEMlRFe7d2OYec1d7cnQyUQJrCuB2QwN0eI1N8jxXUbJXkeYED4dEdInmY64f89SvxokKMQvn0fqSWgojhQoru6G5UoMkrWiUheTR56/P0Q/j+gxH2Z0pN2IEKX9sH+LPDYlbwQ4gqQgLuwhvNedqNnjWw5/VTTgBB4tNF7GGJuxblj1e1OzwOKQvGqRdX66OnR7pMWTOs7G0knYTQZyFssD2YfE12GtnObX+7yN5q9TSTeWUNx2/ARbvOHwWSgQKn86Aw6cgbmoN+MnmxbtEdV8OB29LocLlwOF3O/WMSczxeRp0gu4iLjyVM4F817NUan16lK3pJoxZJoxWg2MGbjF1w+HQbAF4v7s23xHn4Zuli1kpSuWZyLR0I8Sw2nEB0Ri04veVTL1OklZJcLRRZE3jS4Y8jvYvmUderDz+6UuXAomFO7z9DolTBAITZST79meYmLGYSXrxfT9o9m+oHUFHubxcbuFQfw8fOiUsNyFKtUmKTYZBp1qkndeu/ftZKL0AsO6OP75QAAIABJREFUrBaRMk7i8tk7mZwG0JVEie6GpCQSkAuy54xh+jfvodcb6DS0GP1m9OTrtuOQXTJVXymP2XTA8+Jld36B3ebg04ZfkxSXjJCycOWNYsw/dI7U6mkOsG1T307+juy8Bo7TgAni+gNWsKxFUZJBXwwl5gMQehBm0BUG5xn3tMm/QfYl7geB8n/2zjs+ivLr4t9nZms2vdGLVAGpAgIWBFEUBBQVFcUK2LAroiKKgICKqKAogqACIkhRKUrvIL33Tqjpdfs87x/PZpMlSyyv/mycz8eSnT6b3Llz77nn+JT2jHEO0JDuZYjEWSHHEVo8IrYUBk9p8GxSvrGFZZW8t+GPCvJ6BUT857+84j8c/6tMvo2U8kITKv9KJFdO4tlPe/P5K18TnRjFK1Oe+U3b2xxWtGKUPGuEhVuf6kD7B9oQl3xhdoKUksPbjoKEiIBMwtGdx3Hlu9m5ai+JFeJ5d+kblAnwuxMrJpB3HiceCXVa1KJj7+u59s5WmMwm9q4/wOu3vM2JfSdLHDPI7AkE58IM++iuE6z+bgPX3XMNC79cViIz//jZLzi+JwUk/Pj5Ekate4u45BiWfbOGOi1r0v6BNnz5xjTFEEqIVENYUvUlqjWowrGdJ0JMRrwB2WWzxeDuZyTEfhxc5nF5gpz84vhpwnL6vNkMzdjGdxPiSD9jwu/34HV5+Gb4Vzw95nlAld+evrI/pw6qeYHKdSpwcOtRDJ/Bhh838fVWnaiYogfy1Tdn8t24eBAgpYnrH34CzDNUtm9pBt6i6WZkJj9NWIzhF2xatI3Pd3/A9LPjceY6iSsTi0xbCP6iwTEcjwOQk55bzJ5QkHrKjN+wo2sBOmbgy5R5nyBih4dct+FaDFmPqy87qJ0D4AT3aqRrEUq8DJBeMDYRfLXz7lDyuJ61in1jZKByOD/4diOl/zczcC4ILYqQhowI38y/iAvjYrkmDLweLx899Tlbl+6iVZdm9Bx2z+8Sbmp3b2va3dv6d52D3WHj9W9f4IPHxmKymHjpiz7UDWi7lIYti3cos2+nB/epDDRNKzK39vg4c+QcD9R8kje/70ez9o2IjA2vFZJcOZFaTatjMpvwuL28dMOgElIEQBhN+yJIQ5Kaks7xPSdLBPgGreuyaeH2YGP30LajFOQ4uaJjE5IqJXLJZZXo0+IVMk5lIIHkSomMWvsWkwZ9S3RiFJGxDvauO3DeAdW/Wt6YTdVaefSsPwCTJYbnxz3GwS1H8LnPeytCTefO/KIzGUey2LPRH6xQabqBTZuH9HVDmCpxYt8pTh44HSy/FZf+9boNNi6Jos2tmcHPDu6wMXjKEdbMj2HLukupf20bSHsLZK5SHtTiQOYjpWT1PBtetwQMKlbeiy9nErbIDtgdgbJG/GRk7ttgpILjKTSrehlOKBdH9UZVObrrBEg/TdpEoSe9Du5VkDcKVQPXlbrj+cgZRjBoq0ge+H872K5Tmjrezag6vwARpc4dQDgg8yHUg8QKwhLg0mtgqvXHBXhAmOshI3tB/njQkhCx7/xh+/6v4E+nUAohjgCZqN+iT6WUY89b3hvoDVC5cuXLjx0rqWr4v8aUoTOZMngGbqcHa4SFJ0f3pP0Dbf7q0/pVWDljHe88+FEwwy1UkDwfFWqWY+K+Dzmy4xgvtnuTnLQcRLEHAkJJFdz4YBtufPg6nrnq1bDB3Gw1BUsxmi4UO0UIdF3D5/EhhLInLA7drPPV4Y94utWrpJ9UQTyuTCxDf3yVZ696DaEJfF5fCTnk4Qteo0Hruiz4YjkfP/N5kL4ZCoktwsDvB69bBZuoOAc9Xu/GuH6Twtb5r769BatnrcfwG9RsUMChnXaq13cydOppoiv359uPTUwePIOCHCdSSjRNlGDpDJp0iuZt0wEDj1vQuVp9pFTlGpPFxPQTvYkwni+q24sERMxgpNTo1XQ2pw6d4/4XT9LloVRFpdQiEYkLENqFy3Kg3lBWTp+ByTOaVjc50XUD4r5S+vKetWCqjYj/HKGFDqQZaV3BV8SOwtwCzJchLA0QthuRRg4y60VVKrG2CyhcegAJtlvANZ8gY0aUAUtD0GsgIh8qUZP/N0NKAyH+egmw0iiU/4uzu0pK2QS4CXhCCBHCxZJSjpVSNpVSNk1K+nsYHZ/cfzo4ou5xejl9uCRX/e+K5h0aU65aGWyRNmwRVro8cSMRMSUzOVe+eghcUr8K08+MY/DcV0I59FJl/nPHLuLlGwcRG65EJBSrxWw10bJTU6Yc/4SJ+0cxO3Mi768aTGSso0SAF0Jw2VWXklg+npErBtHk+gYklI2gy8N5zH7/IwpyneRnF6gAft6z6eUbB3Nb0kNsXbbzAgFenZSrQMfr1tBNkms6ZdHk6pO06TiHspULsNhUo7g4bBEWbn70emwOK9d3y+W7Q7sYPf8AUTE+ju5P4IvXvyE/uwApJRa7hcbtGtDgmjqYLCY0XSO+XByXd/0aol4FLQmzxUy5Kl50XaLpEJMYgS0ylqILsoClKcJ2HZq9De+vGsL9A7vR6UEnVrsfVSZxKTZNGEjfEaRnPVJ6sNgstOm8j6s7pqJreUp+IPcdlV1HD0JLnFUiwAMQO4qg4qOIg9hRaNF9g3RLoUWjxX+KlrwcYSqPyuj9gAH+FEK+HJkK7pWgWf8zAV5692GcuxJ5ti5Gdv9SrTL/avzp5Rop5cnAf88JIWYBzYEVf/Zx/z+4+dEbWPHtWlUTl9C2+9X/s2PnZKhX4uj4qF9YMzysdisfbxzO8T0pxJeLw5Xv5vD2Y+xYuScko8/NyGP9/C00v6kxZ46cIyYhimvvbMVPE5YUVj0AAv6wBlXqViItpdBDVBAVH0l+VoEagDKgeqMqJJQrYk1Ub1iVMlWTyUrLCe5L0wT3D7yTbn27IIQgOjGKw9t28/FPW3FE+5k59hgmS3l8F4jfhiEpyHFycMsRbA4rht9QGj2awOaw4HH6iqSHheCtr49Qu1EeuklgsR7n0yWSjLNRnEl/kFduWRuUIG53b2saX1c/IDvgxBy3Avz7EBF3kn8wKaRUFx0fybAf++Nxe9U0cr6L9g+2wWyLBnogI7pDwde8O2sEk96Lxe/TuPfZfWjZ94G1LWiRoJVBRPYO7jMqLpK7XroVI2NeYOjIGzDyqFLyHhTMgJyBymhCrwgJ34KpKooX7wLM4N2k9uFeg9SiEbbrS+xH6OWRse8rVpHtRjS9lClUvVJgitYHWBUDJ/IJZN6oADXSAziVREPkr/Mw/qdD5vRX5TMA1w9g6wTWK/7ak7oA/tQgL4RwAJqUMjfw/zcAb/6Zx/wjUOeKmozdPoJDW49yafMaJFYIr+L3R+ProTP5cuB0AO4f2I27Xip9FH3nqj2cOZpKsxsbhQwf6SadS+pX4dThszx7Vf8gx744DENy6uAZ5n62kI+fnohhGPg8Piw2M1d2vYLNC7eTm5EX5JSn7DsFQilv2qPsXHN7S+Z9tggAiQxK+hbHa9Oe49EmL5KXqRq7hYqcJrOJ1JR0lkxZSUJyPhargcUque2RNL4ZXQafp6imq+nK0Nvj8gaZRhFRdu4beCefv6LMwKUh8XkNLr2iBgc3H0FKyZ0v3UDDVsMRQpWfDD/k52oklssnsYbk/VWD2bFiD3Va1KRW0+os/XoVOel5XHtXK/TooinTOi38VG9UlT0/H8Dv9ZNxNotl01Zzbbcr6fx4ewCO7T7BvM8WU7lOBVp1aYaQOSSUcfL08PMa2u5FiORV4TNrQMSOQOYOB/9JhOOR8J6reaMJNkR9+5AZD0LcODXM414NaOA/GFjZifRuDxvkZd6HkB9gluSPQSb+eOHSkPV6cDwCzjlgaYSIelJRKPVkZFrh76gVzA3Cb/9vhCzunyxQD7q/J/7UmrwQohpQyKcyAVOklEMutP5/WdbA7XTTJeb+YCaqm3W+z/4yrAwBKFGxcf0mIwTYHDbG7x4Zkv3PH7+Y0U+OvyDX3B5lZ9yOETzRrB9ZqTkhy6IToph8bAzT3vmOzLNZbFu6i5T9Sk7ZbDExctVgkisn8nSrV0lNSSe5UgKD577M0Hs+5NDWo9S/pg6Df3gZW4SVGe/PYUL/qbgL3FgjLAz7sT9lqibTq/5z+L1+PC4PfYYep2OPDHw+jRdua8TejYovb7aYuLPfrdz98i28++DHLJ+2Bkesg7cXDmDigKn8PHdzyHlXurQCb87ui6ZrlKuWjO/U5QihAm1Ops6ofpV5bVw6ImEWwlQ0gfxerzEsnboaw28QnRDFhH0fYouwIv1nkHmfkpPpptcVB8gKJG7VG+iMWd8PzJdx+nAajzR6AY/Li8Vm5sHBd3HrE/VwnriNL9+J5/QxQdfeadS/Ih+wIJLXI7Tfb4xhpN8VaIgWwgKRz6NFKgE66V6GzHwK1XQ1IeInICyXl9zPuavBCJQhRSQidlRQ5Oy3QLpXIfMngqk6IuqZ0n1lfwek94AqD1mahgyA/dWQng3IzF7q7cbSFBE3LsTE/X+Nv6wmL6U8LKVsGPinXmkB/r8OoSkvz+DPQoT8fD6+//gn3AVuXPlu3E4325fvDlk+of/XFwzwJouJkSsHkVw5iegw+jJ+nx9bhJX7Xu/G0x/3piDXGXwTMFmUbnxccgwT93/INyfHMmHfh8z9dJGaJvUb7Fm7n7mfLgTg5keup1Lt8pgsJmo2qUatZjXYtGAbfq8fV4Ebw5DM+rw+6RnNMEV158UvBlOhVjlskTZuf74T97/RDYvVwitTnqHP6J74vD763TiYxArxJWrrzjwnZaomUb56WYTQ2LquNttWO9i8IpIXu9bg2IFERNLCkAAPsGLGOlz5bjwuLwW5To7uPI6UfmR6N3BOJco6i3dn7APgxu7pjJy9FZn5ADKjO9uWqbq54Tdw5btZOnUNwnQJI166ne8nJLBmfiyvdq/GmZQEiHn7/xXgAYh5Fyj+JuArKhsAwnotIn48IvIZRPxXYQO8+iJrA4H7J31IrTJG/hSMzD4YBd/96tMR1qvQ4sehRb8cNsBLKZW8w+9IJg3nXGR6V2RWH+S5VhjOeb95H38WhKUZInktImkRIm7CXxrgfwl/fVv4IgBVBnnus0cx28xYbGaeH/8YZsuFxZKq1quEyaJ+sQy/pEKNsiHLo4pl9Sazzm3PdKROi5pUvrQCA6Y/T/UGqt772rTnqHxpBSVbrAvMVjNPjekVsq8Hh9yNxWbGHmmjWsMqXNq8Bn6fn+nvfs+oPuPYsngHrnx3UKXS7zeU8xEwa9R8ju9JwefxsX/jIb4bPZ/KdSoUtSBtZhq1bU5S3cloMQOodGkNJuz5gB9yvuKhId3VyL7/JNmpGYx5diLOXBfZqTksn76WBwbfRXz5uKD8Qk56HvPHLQmed61mDRn+ZFUG9arK2RQLt/ZpVqJU4nF5uLSx5JaHU7nvhdNIw0+5amWU21KA/y2En4rVXVjtcH/fs1hthmpwevdQ/TJPsNdhjbBw2VWXsnPVHnasOozXre6HbnaQkvoxmv3/Z+0mjVzIegQ1WyiACBBRiIi7AsuzMLL6InNHgbk+wnJh4xMROwJsN4O5OSLuI/Cug9zh4F4AOQOQrsWBfWZjZD2LkXYrhnPubztffzoyrT3y3OXI9E5IQ70xSulWwm1GVuk7yP8MRdP0qv9mv4j0bCt9m/8hhLArV6e/uRb+3/fx8w+DlJLl09aQeiKd1t1aBs0kfguu79Gadvcq8tGFfnFWz17PR099jsliolHby8hJz6Xb852D1n2FeG3acwy6YwRZaTnc/0Y3Oj9eJFJ1ZMcx3rxjBI6YCB4e2p3xu98HVNPXbDVjd4QqBLa/vw0NW9cjOy2XGo2qopt0xr88mVmj5uEu8LD2h428OvVZVs/+GWeem5ikKDr0agdAxqkMPAH9Ga/bR8aZLOq2rM3TY3ox55MFVG9UlUfevS/8PfWdUNm0zMfmj8NqT8YbKIV6XF7ueK4zx/ec5KfPVbPY5/FxYMthtizZweTBM4grE8U731fl0Nb9lKlxJXXbPK1kb410pYMiTPRt9xIHt+Sxc108VWu76DcmnfRTmUQnVFLaJv4UQCDMtfkueyoiswP4jwTO0E2Nunt5fcYLzB+/hOqNqrJ73X7mfLoQr9uLbtIQmoZu0qndrDpHdhxj7Q+bqHpZpaADmJTuEImAUuGcBr5jqFIMoFdGJEwOljFk1jNKOhcfMnMbJP2I0MuVvK/Sj3TOAiQi8gmEtaXS1CmkRAZr+dchs18G93LAC9n9MPzHEPauQTXN0iDzPwvcPz/4jiALJkFEd2TaLeohChD/JeJCYmV6ZWVvWBy+nYqueRG/GheD/B+Ez176ih/GLMDn9fP1sFlM2PvBBaV2S0NpWUF+TgFv3fNBUJWyIKeAb8+FH8uuWq9SMHgXR0Guk2evGUBBTgGaSefQtqN8vEFNQ5bG6ClbNZmyVYsEnLYt3xWkMUpDkno8jcnHPiH9ZAZ5WXm8/cBozBaNLj3WsWCirjjjws4VHZSI1zW3t+C6e64OO2S2bs4m9m86xM3dVxEbpUwqPntdx11sFuve124DlBfrym/X4XV78bi8HNs6n8X50zm5L4odK62cO1GTD1bN5eCWIzxU50nys07Ts/9p2nXTcdknsWddCoahGr0Hd0ZQpsI+nrmqP+N3v09i+WnIgqmAjoi4C03TMWwdIP9jgpQh11KaXteJZjc+T9rJdCYPmRE0WReaoOdbd3Ptna3ITsvlyZavBO/ZFR3qMXDCJoT/AFKrCnHvI/RKCM1xwe9AZe/FZhX8B8CzEWyBGQ7ffoIPAKErpynna2pa1tETLaKb+r7yRqvhIlxKryZhquLGu35ENRAtiELVSd8hgsJnuCHvY2T+55A4p0g22cgE/2nl0SqK95DOk6OQUjVvjXQKJ3Jl3keIuE/CX23MQGTaITAKG8kmsPw9GSx/Z1ws1/xBWD5tLa58tzL19vnZt+FQiXW8Hi/v9f6E+2v2YcyzE/D7w0+KXggFOc4QGmReVgGpKemsnPkzpw6d+VX7SE1Jx+8zkFJJ5x7cfIR9G0ueKygZ48lDZvBC2zeYPXp+SF31mjtaYg34gXpcXj7v/zWpJ9JIqBBP33aD2DB/K2t/2MyIZ2Di2j00vTYbr9vNKx2G8OClT9E5+j7uKNOTw9tDh98WfrWcIXeN5Ks3p7N8+haMwEDRijkxwSzeHmWjzhW1AKhSpyKTjnyM2Wriqo6ZDPtmH48NOsmnS/aRUNbN4W1q/yN7DaJF2x00viqbD/uWJf10Fhb/BOLLOdA0idAk0XE+Th22IjTBwS1HEFoMWuQjaJE9g8wTYWsHFMu8/fuR6bdi5M/CERMRoswZkxhNtxc6k1wpkW3LdoX0SDYv2smWpWcBA4zDkH4r8lwrpLukf24Q9m7nTa/KQGAPwHYrCDtgV1OpBZMVJdN/FHIGI72BrNizhuK2g3h3IGxtEXFjwfEoWK9F5o9HerZARA+CtXu1sZIzdi9XZ+DZiExtg8zojkzrpPxpC/fs6A16OZT3bGWE417QYigKO6aAWXl4CC0OLXkuIvYjcPSB+MmgxWPkjsLIG6PKVxfxi7iYyf9BqHNFTTLPZitJXZ9BlboVS6wz4705LJ68Eo/Tw5yxi6hSrzIdeoY3rM5KzWbBxGXYo+zc+FAbzBYziRXiaXHz5Wz4cQtSSq7v0Zqe9Z4FVB182E/9uezK0k2Wy1cvQ2xyNGeOqD9yKSX9bniTGWkT0DSNvKx8Vs9eT3RCFGeOnuProbNwF7jZu+Eg8WVjueb2lkgplRvSNXWC0gQF2U6+HfEDtZpWJy9bMVqkAedSzHi9sHp+LH4fgI+U/acBpb3y/mNj6TXsXiKi7VRvWJUV09cGnbCmfFCGa7qYiEs4Ta1GFjYv1/C6fUhDUqFmWUb0HMPiySsoUzUZV76HW3qmYYtQDyJXATRtncP8ry0MvWcIg79YR0SUD59XUL9lHPk5OgmahZEr32bci4PIzzjEZVfk8O4zlZHIoFH4+RDmehA3FpnZE/AEGtJeMg69yd6DZXj8g4eYNnw2ZpuZvhP7BLerdXm1EqqemanFx//9gBOZOwhhDV/7FlokxLyDzHq68AOwtg3UuPeBZx+IWLLSnJw7kUti+U3EJxfL7H3HkbkjA9z2QhEzArLEIKwtkPkTwLNaXZtnNTiepaRDlQcZMBmUue9T6P+K/4yq6du7Bg6ZCImLlByCiFKCZrYOSnbBvUBJIES9EPZai0N696i3p/zPkFp0oFcikK6fEImzf3H7/zouBvk/CM9//jjJA6dx+tBZbnv25qBZd3GcOnQmWGrxOD0smbIybJD3uL080awfGWey0HSNDfO38OZ3LyGE4LVpz7F/02GsdgsrZ67Dle8KjtjP+WTBLwZ5s8XMu0tep0f1PsG3goJcF163FyEEjzZ5kfRTSoMluVJC0BnJne/mwJajXH1bCwbd+R4b5m/B6/EFZRN0szIBH/3k+JBgpmnQr1t1hCgUwgrFoS1HeLXjW/j9fhq1qY8QRc5XBXkWUtJGE1+3Lq9MdzH+5a/YtGA9MYmRzPtsMcu+WYPX7ePkAfXQOLTTTq0GzoABB6QcsYKEtGNrsEYIzBYwWyTX3JxFRGw1zqV3ZViPD8k4I+nx+kt48s/wxqSvwJfC290f4opbe1GvVW3SUtKp1aw6x3enUKFmOcpVa0YhL1oISD9j4pG2VfH5RmEYOq9Ne47mNySBbw+GOx1hnKVW40b0eOMOJr35LbpJI65MDC1uzAayUGWNwukzD0ZmHzVw5HiwhA6MsLWF+EmqVm1pAWjIc9cEatyqPBITD7EJ4PWA16NjttpAi1X1cc861ANFV9r0MW8gTEVGJ/h2UMT51sG9lJIccANyhyNtnZQGD7rapxAgQoeqhAgVFRNCD4ilhQqmXQjSfxryPyUogGYUc/3y7UFKz3kloos4HxeD/B8Eu8NG77fDNxAL0fGRG5g/voj9sXvNPs6dSAtaxRXi9OGz5GTkBYd/NhYzpxBCULup+qM8sPkwZpsFd4Ebi91CxdphhmfCoEyVZK68pTmbFmxDAq06N8Vqt7J3/QHSTmbgD6hKnjp0NhhwAbYt3cG5E+1YN2dTsO6MUAyZ2s1qcNWtzVk0aQUUK0sYhuD4fjsRUTZskXqwpFUIv88fLGOsn1fE/9Z0jXotL6Vh63oA2COtSPdCUlMMTh7MZv+m6fj96rVfGpKK1Q0mv1cOTYOaDZz89E08O9aqEsuJA2ak4Q+cjxlHUkv0+HEMvLwvh7YdRRqS9x8dy4z9bjTjACazpFbDHB6+egITsmxomsDj8mB12DD8ft758WZq19BUExdY81MMbpeGx+UDfMwY8TXNmiwIXIkTiR2E4N5+39D+/ms5feQMtZpWxxZhUwyTvPfBOV2JgPlPKw1390okXkS4CVJT5YD8b0Vkdn+QWRR/sha2dTwuwXcT63HPgCfA0gyZ/yVFNX0DzLUQ5zsh2W5SOjXSD1jA1hG8Wygq7wQg/UofPvo1ZGaKKgnZuqip3j8S0k9oclComCnAVPVigP8VuBjk/4eo3bQ69ih7UPJWaCIYUIsjuXIiJrOOEALdpFG9UdWw+2t37zWc2HuSlTN/pmHrutzZt8uvPpfXpj3HtmW70DSNBq3VH3qZKkkh56PpSmisMMgf3n6M7HPZyoQkgJiEqGDz1+P2klQxkdSUNFz5blXDl0rE7LGRD+DMc3PJZZUZfNd7uArcRMY5yE3PC9GEL4ThN9i38SA/z91E8w5NwH+EvetdeFyK+WMyG/i8AovNQNMkT799mAH3VeOjV1WZTPULVAaamWqm3501GTGvMqaICojIpwBIO5kRfJvRdQ2dw+hm9bNhQGJ5J+lnitpWhd+bO30c1DAQQvUSkyt4CMR7zFaN1JQTdKhcgzKVvAz9+jBlKztBgnTNITGhDImWIZAL0lkPtBhE1DMQPRAKvkTmFqosOgNMmdAgLz3rA0M4AkwVwdQQlUmrB2fh+5KUKtjXv+FFhC3QrIy4A+mcEhBJM4O5MdK9CiytgiJbIupV9bl3C7gWQ/6Hgbq8F7z7wLtNHUCvDHoFlZmfpx//R0KYKgakIr4CNIjqD/5DgAkR2euXNr8I/gNB3uP28lnfr9i7/iDtH2zDzb1Ljnj/L/HURw/zXq9PQUpueKCN4mSfB7vDxgerhzB1mGrm3fdGt7D7EkLw0JDuPDSk+y8ed9vyXaz5bgNWu4Xr7rmaKnUr0bhtKHUtrkwsl15Rg33rDyIlWKwWIuMdQc0aaUBS5SRenvwUo5/8HKvdwktfPRnc3mI189GGYWxetB3DkMweNY+M01k0aF2X0U9NwPD70U06H20YhslsIrlyImt/2MSnz08kNSUj1BxEgCvfzeC7RnLTw9fx2Hu30Pb2bI4fMuP3BR4yQvLOzANUqu7BEWVQroqbwwFzDr/foG7LWhzadgwhwOyogzlpoCpruBcj9fLc1e8WJvT/Gk3X1ABVRDV8+TPweb3k55jISi+DpheEnJfJYsLlTgbOAh5cBYLl38Xi9WiApEb9PA7vsuL36Zw5ZmbUyxUYMvmIaohq5SH3TYLZtG+7uq8ZmxFJK8HSEhWwNcCqeOznQeaOIGig4TsO1q4B8+/9anJVFk0v26N06l++FlBBXuhJkLQY/CeQOe9C7iCV/1uuQsSNDvxOaUhbR6WPU0hzLPgSkbxWNX3di9XxbddfUFJYupYivdsQ1qsvPIwFqknrWQd62VItCrXol5GRfUCY/xYG4P80/OlSw78Fv1fWYNeafRzYdJjG111WwjB7bN+v+O6jH/E4PdgcVgbO6kuTdn+txkZ+TgEel/cXzT92rd6L2+mhUZvLggbT4XB870kWfLGMslWTualn2xKWgJsXbad/p6FBSWCz1czQH18NlkKKw5nn5Ks3vyX9VAZ3PN8ZoQmG3vMB+dmbNyRuAAAgAElEQVQFtOzcjLzMPFp0akrbu6/61df7zNX92bV6X/DnctWSGf3zMKITooLHfLxZP04eOI1u0qjdXD1oCqWNHTERzM78AulezupvhrF1pZOfF0Zz5riVS5u4eXfmPhCSF26pzt4tqkRjspjo/82zLJu6Gr/foM+HDxGXbEKm3QRGHiAh6imOHLyO7NQcLru6Dtmp2RSk/UBsgh+ntzUWRxJf9h9OpUt2cWRfDbYsd1KtYVX6ffEwdoaCdwsrfohlxNN2kC6qXuqka69U3nu+Eq4C9R3Ua27w3hw3WK+DiIchNdzkuQ2ROAthqo7h2Qm5Q9UUq/12hKNXkFYrjQxkWlcwThXb1oSI/wJMdZHp94D/PF65iEErsyHkI2nkIc81o6h0oyOSNwbpm1IayLP1ii23IJKWIPRf9kA1nD9A9quo8o4NEf8FwtK4xHrSyEemdwIjU5VkovqhOX45Wfk1MJw/qMEuEY2IfQ9hLr1P9W9AabIG//hMfs13G3jrnveRhkTTNT5cMyRkMGj/pkPBZqfhNzi+9+RfHuQd0RE4foFCP6rPOBZ+uRwhBHVa1GLYT/3DcugzzmTy5BUvU5DnxGwx8ekLXxARHcFznz0a5KTP/WxRMMADeN1e5o9bHDbI2yPt9H67R8hn43aOZNGkFbz/6FjcBW7WfL8Rq93Clbc0D3vuh7Yd5cDmIzRsXZdy1cpQu3mNkCB/+vA5+lzxMhP3f4imaSz6agXnjqcpK0GfQUxiNLrJhM/jRzdpVKylBnqEtTUVL6/G8CdeDpp3HN4dyZFD7di9PoN9W/0U1qb9Pj8fP/U5meeyMQyDnSs2M+ALB3UapiMCwcvI/YJqtcpA3VpsXLKLgbe9g6brxCZGMWZLdyIcmTw1+Ecl+yuOgeMxtMhHA1cxAoCrevjwm2fhyxpJ607pAFQa4+b4gWg03UTvkf3REovMXgzMFPHOC+FGYlZFMO+GgMSwS/mz6uXBrjJ6mdEzYLNXHD5kdj9FnwwKkxVCC2+aLewqKw+agNiVLnrhYqEhHb0gfyJggIhAZj0B0QNL1vDPh2shRfV7D9K9AvRKJf1TvRsDAT4g4lYwHv6AIC/9qZD9CoqHfw6Z9Tgiackvbfavxj8+yC/4YmlwwETXNX6euzkY5E8ePF3EVxdKnbEw8P2dIaVk7thFwTLBzlV7AkJgJY2GD2w+ooqvUk2Uet0+XPluBt0xglmZEzFbzOxZtz9kG13XqHpZ6BuPlJKtS3fiynfTtH3DEpIK21fsLmLaFLjZtWZv2CC/ccE23uj6duCBJHh16jPUbVGbWfo8ZDGlyrST6WSn5RKXHEOEI483Pt9HQhk3q+fH0rX3JPbfa+fL9xoRW7YmfUY9HNyufPUyRERH4HX7lIet3cJbvQRxZauBOFDUf5Rw7kR6cLvMc25GPZ/Fh/P8mC2qZi39Z/Cmv4LJBBu/vzz4e2T4Ddb9sIm2XQudnvyqROGaB8Egr2Aym2h7T3tk6hAKBX4+mJ/F2bxxxJeNJSJK6blsXrSdM4d3cvW1IswD3oLwrFENVc8eioKkE+k7WNQB8e0hZBiqEEZWoM5e+CDXgCgwlYeol0usLoQO8V8gs18F3wElspV2HUb8dKWto0UjHI8g3csDBtoe8GYhM+6H5J9LN8mwtAhw6J2ACfLHIvM/Q1raQPQANFOAdaaVDzRVAXRV4/8jILMJGf8xMi+46n8F//ggX7t5TTYu2I67wI3ZaqZaw6rBZVOHz8YdyPiEJrj50RvC1sD/auxctYd9Gw6yb+MhDmw6TPObGhObHEPGafULKjSNqLjwk5DVGlQJa3TtC7BWzBYztohiwzsCrry1Obc/1ylk/VFPjmfhF8sQQlD1skq8v2pwyDRqq87NWDJlpXLLsllodmPJV3BQ6pfFDT0GdBmOPWBg4sp3Iw2J0ASxyTFEJ6jSSuubZiA9uei6pErtswgBDVu5GNFqDSJpEEIv0pux2Cx8vHE488ctYv74JZw7kUZeZj4F2Slomo7f0NBNGvcPvJOZH84j62x28MIP747gyQ41GLNQ9RwkYDKpgHpd12PM/CQBpHqIHdl5DO5sUSwQ2ZQ3axgILRoZNRBy+gF+dFMsFWrEBgW7vnl7NpMGfYuUPiZE1OSz5XuIjjMI4arrlyjfVfePgb2aQZgQNiVnLP1nCUdBBQHWtgjHfUjXd4F6faBu7T8Bmb0wol6Egi9UwIt8As3xEMJ8GdLaNjBM5VLSuZndkUa2MhyxdQlMuxaDzENlyEVCZFLKgExENEJYEBF3I4UJPBvAvQJkIMh6foK0xRjWtojYDxHmmsjoNyH/E9ArImKGhb234SD9ZxVXPpzVoF4NLI2VfaE0wPHf0LcvDf/4IN/txc74PD52rNxDm7uupPlNRcHHER2BbtbxeXyYzeawmfBfjdWz1zP03g/wenxBFktqSjq3PduJTQu24irw0OfDh7BHhpdwTaqYwMgVbzLn0wUc253C/o2HEJrg+vuuxREw8n56TG/6dxqKz+OnRafLeXXqsyXkBH4cvzhY0jm8/RinD5+lQo0i3ZMWN1/Om9/1Y8fK3TRuW58G14R/ba/WoAqrZ/0cZMzIgNGHxWbmoSHdyUrNxu/1c2ffLsHegSaPgx4IdsXimNfj4+cZK7j6jttCSlUJ5eK497U7+OGThSChyTW5vP75EbIzTKyaG8vZ9Hu5++WueNw+Jg2aHsLbP7Y3grULomneNgefV2AySdxOwdF9tpD1fpqwlJ5D74W4T5W0gbmmmuA8D7mZecx8fyYduo4gPsmP0FB0QucPENENKd3M+2xusLyka4LdGyJpcYObIPcbP9KzNSCXUJjF+yFuGsJcR/3o+vH8QwNWiLgPEfW0ohImLQN/CtKzQdX1Cxu0ucMJct1zRyCt1yJM1RRlE1PgHDT1RoBXZe5Bu79CmNTDRBQP8G5kRg/w7laUzvhJCPOliIhuENENI/UG8BfPpH3gWaUCsKUJWsQtEHFLmOu6MAznT5D9gjJNMdVSxyxGoxRCg7jxquSlRSJMNX7T/v+N+McHeV3X6THgjrDL7ul/G7vW7OPgFlUf7tAr/HTpX4mFXy0vYWXnLvDgcXkY/fOvy25qNL6EZz55BIBje1KQhgyZuG14bT1mpE3AlecKNjvPR2KFeM4cSUVKic/r54lm/bBH2uj/zXPUbVmLae9+z+aF27jylubUv7rOBc/lzr5dmDt2IanFSiWgeO+XXFaJKzqG/lFLIyfQCFXIyzJjtXtBCpbMjOHjATPJyYoJy4q686UufP7KFB58+Qy2CIktwstN96Tz1cjl7F53HZ0fb8+Pny9RPrKBUopulqSeNOPxwKQRZWl3eyZ7t0Twcf/Qh6gjWv0srC0R1pZhr1X6DpK+62463pZPTLyPoiqGF4RZSeym38oltTXOnYjA59HwGzbK1+sK9lxwzkY9WXxQMIVQLroBxa309GTUn6svZB1h7wzSi5HZCzxbwdoCrO0JcjoxExqsvUh/WmBoyQ+mS8B3RJVL/MeLrVfMFEM4IPp1hK1z6A1wLQi8CSipA5k7HBE/oWizmLcDdM/som2kVC5Tvxd576hzk6hSk2cjWFuFrCKEDpZGYTf/L+IfH+RLQ1RcJKPWvvVXn0apqNOiJht/2hasd1vtFoSmcX2P1r9rf1XqlJRTAEVvtFgv/Mf11rxXGdn7U3Iy8kjZf4r87ALyswsYeNu79Hj9dr4aOD1Qi99PTFIMre8oCnyuAjenDp6hXLVk7JF2Hhv5IMPv+zBo4p1YIYGbHm6r+O7nw7tDjecHsuiHrqyF1a4y4txME+4CL5sXbg8b5Ls+1ZEG19QlIeJlpNyKEIq7fnhHFvPbD+bbs+OYuG8kCyd8yicvrEQ3S0xmyVUdctD08nw3viwzPjmfMSJJquDj5Y93Iv1pJRuGxdfMfIyK1bLRtGA5vgi2jsr31DjH8yOdfDKgPKeOxtLtlZeo0vhSZOrVhNTXzTXB6wwMNkGwnm1rj7BeBdYbIWIbFHwDFDpOeZUNnaWVCnZ4VYnEvQL1SmRT5YsQxo0AEY9M7xLI3HWwdwL7vZBx2wUu1I+wtglTi9eLXbgGmAPlmzNKxsDSCJLXI73bILM3yBzF1ddrInOGqDcA+51oEec9PEqDiCFY5pL+gBbORZSGf3WQ/yuRmpLOh49/Rk56Lg8OvptGbcLzgG9/rhM+tyo3NWnXgPI1ylKrafXfVFpKOXAad4Gbag2q/Gpta7/Pj9BEsGxTsVZ5RiwbyOnDZ+lZ/7ngejkZuSyYuCyk6Xpw8+FgkD93Io0nmr2E2+nBbDExat1Qru56BeUuGcyx3Sk0vLZu6faJpqrF6t5mqjcw2L3ehNupY7EZxJeFFp0uzLWu0egSpP99jIxHycvYy7zJCWxdFYnZ6iU3M48407106HqQxpdbOHHQyqVNXEQnVUXEfkKvt7cz9sUvg6UlsxWat81mwPhjgBlc34Hj4RLHlP5zyKw+4D9GGBFNQNWnpZYI0sARZdBrwFnMjso4KjdVbkchLuUaIvYdZNaLAfEwNTmLcybS+T0yqi+a4x5EdD+ktaXSrinUizEKoGA6RYyd4o1ZQylVFofeUMkbGPkEM3zXQoSpekCNJhz8agpXO4+VZrtB3SP3MtCSIaofMrN3QDpBQNxHAa58I2Tyz4CBEDpGziAomAa4wbcTaaoUlmYZDiL2XWTm44pl5HhYaQldRKn4V/Dk/4547PK+HA44JVkjrEw68hGxSX981jHlrRlMHjITTRM0u6kxr33z3C8G+i8HTmPKkBmYLWYGzHiBZu2LXm2llLzV/X3Wfr8Rt9OjpsfNJgyfH91sQtMFby96nbotlArkhP5fM3X4bAy/gdAEnR9vT58PSwbG0iDd65D5Y0GvyBvd91KQo+rlezdF0Kl3eZ74aNSv2s/Qez/EKPiJK2/KIC21Gl2f6gJ5/c9by4aIfg0RoUp8bqebE/tO8eP4xcTGbeW2B5dhtXsAe2C920scx8h8PKDp4kcCfp+J/NwYouI8aJqdDOdrrJkLVepWpH7T1Xz6wiy+nxCJ0Ew899mjXHdPS2TqdcXs96IQSSsANzL7NfBsB3mO4IPAVAfMl6lgamoE5IFnEwhTIJN2ljjHC6Pwbc4X2L9Z2dc5HkVmPhrYV0CLpjgSfkQzVwu7Rym9CGEOTOP2LnoA6VXRkhaErutPRaZ3Lbr2876P3wIpvci8MeDdiYjoFlAI/W/iX82T/7vi1KEzQdaLpgnSUjL+lCA/adAMvAFTjp/nbOLc8bSw4miFOHsslW+Gz8bvM/D73Ay/bxTfnh0fXC6E4JUpz7B69nqG3zcqqDVjsVt4+K3uNGhdlxqNihQaI+McmMw6Hr+ByawTFXcBM+hSIKwtlAKilPQZehWOiHQQsOy7GGq2LBlkL4SXxjXFnzEWXfMgZTa5KVuIjCnScgHA2hLsRfIPVruVGo0uoc+onhi+05C+RjUehRVpvS6Y20opkQVfQMGsgGmICoICG6aYh4mt8ARCmDh7LJXeDZ7H5/UhNI373riDOV/G4fN6AR8fPj6Odve2RpqbBJg0UjFbnDMRjh6IuI8wcoZBQTGfACMLnN8DbvAsAnNzJVImIiC95FRs6SjM+DXQKoP1SkTUc4ohZO0I7pmUpGlaSrW3E8Eau5VQqc3Q6VRlqXhHMa6/UA+q3+EtCwEFzIKvABfSsw7ivyrVDeuPhPQdU9O/+iWIQj3/vykuBvk/CTc+2IZ54xaDECRVSqBKvfC18v8vHDERZJ1TjS2f14/FXnpT63y6peErybsWQtCkXQNMFhOiwIPJolO3ZS26Pt2xxLqdH2/P1qU72bZsN3Vb1qLbi7+hvlri5FJJTC5SU7zx7kxEmfCSDuEgfNvRtUDwFT6iYgN8+MA/RDyNKfaJC+/ANVsFeFBDOtl9Mbw7FAtFmMMMG5lAL4MIlHRkwTdkHtlAbJKTU4fVGsumrQ3R4Q9OLms2iiiUAulejPTthoh7lGRwyH1Joyg4S/D+DJkPQuw4VC28JIU25BxDmrWF0MB+A1rUi8WOczT8vmwdQK9U8vPzYW4A9jtV4NXiStIijfSAYUixYYaE2Qj91wnrlYB3MyHNat+e/4lrlPSfQqbfEkgGTEj/s2iOB/704/5eXDQN+ZPw6HsPMODbF3j200cY/fPQUv1a/z+4d8DtQdqhpmvM+WRhqeuXq1aGDr3aYTLrmG1mnv6kJC0QICLKzqi1b9GxdzvueL4zA2f1Dbue1W5lyJxXmJM3ibcXDrgg1bM0eD1eRj7yKb0bvo7XU0xNUYtH0y4s53A+MjPrYhimkGq3lLD2pyj696jF+hXhJ3TVelLRHoNMFB94VoLMAONYmACPEuhKnIPQIpHZryBzhlCz9lze/24XEZF+LHYLl7WsQI/nz6DrEqvdoO9nAeqp48liTcQAr9w5E9LvAN+a8+9QmBP2IHw7IOr10M+FnRBTkxIB3qGOqZcHBIZnR9EiS1NK5H32Hmixb5coAUrvPqRrEYZnE9K1GGnkIYRAi34FUWY3WvKaktOxWoL6BxNgBlNdNNP/YwjK1gXF2Ter5r0lPAvqD4fnZ9SDyqtoqs7v/zfH/Z24mMn/SRBChNS6/yz43D7MZhNejw+fx8futft/cZsnPniI+97ohtl63qDUeahYqzxPjwn/EPg92LxoOztW7SWhXCxN2zcK2gnOGDmXRZNW4HF6eP3Barw8Joeo+DhEzK9nRq2csY5h931KnSbVGTJ5N+YAdVoIqNu0gBY37Cc7ZyRSfhksPRjuFeBaAtbrlCGHP6XYHgVgoTBTlBJOHrZgdxgklPWpZfZbi/xZ3UsBF0KAI8bO9d3LY45qzgOvWDG7M7j9sZNoGghzIK/KfScw0m8GCijKbn+tW5gBppoISzOka25ADtirTtR+FzgnUzLAxyPKrEC6VkL2Y5A/FvLHYsSMRLN3BEevgHZ7IUyI6JdKHrnge8jpr84BD1JEKM34xLkILeqCPSEhdEiYrspemBGOB3/xKqVrEdK7A2G9NticldIA3y6EtRmYRikqpfVahKnKL+ztD4KpdjGKqlUNX/2NcTHI/8PRtH1DJg6YCoBm0mnX45pftV1h7fz43pOcPXqOeldeGhzB/zOwcsY6ht8/KjgTYLaaeG3a87Ts1JRTB08H9YU2LbPx7cS7efit36ZjMnHAVDxOD9tWm0k7baVcFcUGMgyIivOjaRAXuxvcC8F2E0bBDMgJjPw7pyAdTxIyiSUqg7kcvoLNGH43w/tUYf3iaKSEx4cl0rF3Z7B1ULV6mU/xgGoyeXh89AtopqoYns3glqi5LxtYmihPVPf8Ymev6IcqaBbp75QOidQrQ+r1AackA0x1EVF9kVoCuAqHwIoH+gxk6vUgzmM75Y0Ge0fVPEUjRHO+2D2RvoNqajb/M0LKJLIApERm9kQKKyLyaYTlcqR0gnevmmjVVZ9I6EngeAiZ+xEydxg4HkVcIJtX39GbgFM5ViVMAlN9ZNZjisEjJUQ+hhZOc/9PhDDXhdgPkAVfg6k2IurJX97oL8TFIP8PR5W6lRj981A2/LiV6o2qlpAPLo5ty3axfv5m6rW6lFZdmrHsm9W8+9DH6CadyDgHY7e9iyOmNCPp34/l09eGDH153T6+GjiNlp2a0vGRG1g6dTWarlHzsjxu7XkK6dkSzNzcTjd71h0gsUI8FWuFr9/GlY0lZd8pDEMy/KkavDMjDbO5AN2ShPQHCuQCkIHSR/740B24liq+uPNb1dCMfRNMNfnirREsnrKL9LNmCPjNjhuQSoces1VN2LUMjJTQfeGHjB4YEd0hbxRK5CtRMWQinwpMrxaTNACIek65Hnm2g/fXCGpZIP1ekKeLPvIdRZrqQEZX1cwNq3OTCqbY0M+CNXFBaE1eBs/RyJ8cUHbUUeyb8xk4roCtoB+ZuR2ZMAsyHlDceAyIG4cIyELIjAfAdxAwkK4lkLwivPmHeyFFzCEvuNepEpd7LcGHTN4Y+B8HeQBha/O3b7gW4mJN/l+AKnUrcftznUoN8DtW7uHVjm8x7Z3veeueD1g8ZaXyb3V6KMh1kpuRx6aFAX1zKVk/fwtLpqzEmfdb6HkXRoNr6mCxFfUlhBC4nR5WzfqZWpdXY9zOkQye2Y7h0w8QG/E5MuN+pHs1nqypnNpwJam7H+GZK59j0eQVYfffd8IT1Gh8CTGJ0bS89T6sFVeildmEiB2JENGARemuB7RgMFUN3YG5JlrMYESZ7RA9FDJ7QeqV9HhqDrf0PBcSj20OP3iWqwajcYLiwTAImQd5H6IyaQNkmso+s18JTK8WlskEWK5WejLWVuBdXfqNFAmglYXI51W/IAROyH0b/Oe4cNnHBPY7QK9BkGET+0Gx5cV7IBrS+R1SeookFwrfWswN1bZ6LdAvQb2JFB5TqDq1kaXWl05k3kfqtshC8/HC+1IQON8wsLSiSCdHvWVImUvIvb44DPWLuJjJ/0ewdelO3C6VSbsL3Kz7YSNlL0nm2J4U/F4/hiFJrKhe48c8O5H54xcDMGnQt3y67d3/d+O402PtkVLyw5gFnDuRijvfQ8r+0wy/bxT3D7yT25/rRFLcSSgoHKf3IfOnIJzLqVLLQ7nKAp/Hx6Q3v6XdPSVLUsmVk/hoQ0nfUGGuA8lrVMDRkorqxdHvQMZdymXIVBeiB6v1hQWZ+waFY/0Wi4vbers4c8zKwukJRMX46D/2WJgr1ChyaApMY5YYLnKBewky+i2I7K0Ggky1ELHvAiCdMyjpp1ocFqU7r5dVDI+8d89bLsE1l6ISTeHxdbUMDaytFKfccW/JeyWsyIj7lexvIXIHI53fg5YUYPlIkAYiZnhIDdzIfj0g0wBoiapuHVBHBZPaHvVwl5bm4NkCGKAngV427NWKiB5ILMpY3L1KPWjyRoGjZ8Au0YGIHVHK/boIuJjJ/2dQ78pLA5Z4YI2w0qRdA54d+wiN29an7CXJPDTkruCA008Tl+LKd+PKd5N2MoNju84vR/x2CCHo8sRNjNs5ksfffxiL3YLhN3Dlu1n6jcpehbkBRZmbXdHwAqP0Fpukah03SZVC68nStQQj5x2ke13I56tm/cwtcffTJfY+Vny7EaEnhzQENd2BlvSDcmTSIpTUbt64wNJQRo9ugqeGn+KHw3uYsmUfdZs6S6wDAhJmKrqhuREi9n2I6hdmPS+k3wqWqxEJM9DixyG0GKR3l2LXBLNUDYil6E9UgLkpIhAQhV4eYt4Bcf5MhKtoH6Y6EDMGETsaUWYbWtldaHGfXNAXVUoDnN8U+yQgsezdoI5luky9RUS/XqLJKaLfQMSOQEQPQCTMVOqZ9jtAxIK5CSK6SPJYxI1FRL2IiHwGkfDtBTn4Qgg0x10IS1N132Qe4ATfbrTkVWhJP5XqKPVLkNKD9B1Xbyr/YlzM5P8jaHJdffpPfY7Vs9fT4Jq6tOtxDUIIhs5/tcS6FWqW49DWoxh+A2lIEivG/6HnUrPJJUHuuDXCUqRoaesARi54loLlarC2R3MvwOcx2LLSyuDe1RD6QX6asJT2D7bBcM6F7JcBF7LgK4gfh7A0x+/z89Y9HwTNxoffN5qWnZuF1e6ROf0Dui8+yB+FtDQOCGs9hqoHm4EAPc/SWPHYpRcKZkB+8UzaD0YuWuzI4CcCkBF3qYZlejdUlm4oPnrGXarJGfcRwnqtmmAtnvnrl0DUK5D9dCC4SSjsLaAGi4StNeBRU7LB2nVhrd8MlhZo9t9irO0tmlYtfhVaIsJUA5E444JbCiHgvIlTEf0qRJf8/RLCBo7STe9DoJel6GFp+VWcfSm9gOmCTB/DtRSyngF8quSTMAOhlwu77j8dF4P8fwgtbr6cFjdfWAemEG9+9xKjnhhHdloODw66+w+f1K3esCpvzOzLvM8WUb1R1aABuRAC4bgLHHcF15WJ8zB7VjPsyW9xFbgAFx88/hktOzclUle0RQUX0r02GOSNYubght/A7/NDOIE2/ymC5Q3pR+aPU1lmma1I/0nFRTddijAVTflK6UNqYbLhAINEGjnKINt7GJyT1FSnXjkQpAvPKyAxnDMYkXRtgIZXmLXbIeIOhLk2Mqjro4FJ6bRIz8aAuqNL+cDq5cE4rdgmerLSkddrI35lQ1K6lyNd88HcBGydlTkKErQ4Ze5hqgGetWBthZRuxebRypRuHvL/hJHzjup56GUgehjYblGyDpYGiMhnL3wtUiJzBgTKOZEQN77EFKz0HYGsxwn2EIx0ZP4ERPQrf9r1/JX404O8EOJG4APUo3iclPLXuwNcxB8Kj9vLJ89NZPea/bTpfhXdXugcNtNJLB9/weGnPwpNb2hI0xt+eTpR6In4TB0pyJkS8rnb6SEyvlXAbk4ZZRSaRltsFp4d3ZCti5exYWkMHXp3we5QI/bSux9FN6ytrt3xhNInx4dicCxGutchkhaimSqCqeSkssx547wBGAF6VYR3B4aIhrT2xdQkCQy15oH5SvCuJ0TGN5C9C3N9iPtMcd5NdcHeFaFZkNZ24J4H+MG7Fuk/qxydCm3zXAsgdqSiKroWBKZWrWBtDJiQ0h3k8kvpC/Qm4oMBWj0wngRc4JwPetXAOQkwTGDsA99WpGsOMnoA5A5TzB3TJRA/VTlJ/cGQni1QMEmdk/8YZHYHLGDrgIgZVro2k3db4LsxQOYgs19GJM07b51dEEIVJTBE9u/EnxrkhbJt+Qi4HkgBNgghvpdS7i59y4v4MzB50Lf8NHEZHqeHkwdPU6l2eVp1Du929HfCJ89/EaI/06LT5SRVTEDKW1XJw/szwtpOSfICRt4oru8ylXadJJIUNLEPI+0b1Qx0zQcE2LsgYt5Es9+ANP9fe+cdJldZ/fHPudN2ZvtuNqGGjrQg0hSkS0dRQBABRURDERABEQSkiyDSVKRIsUEQFaQjIIKKdL5L+rcAACAASURBVAGpAvkhhJJsstlsm37P749zp23PZkt2836eZ5/szNy5970z2XPfe95zvt+70UVfKqtW6UaXnAKNgTZ69hnQHEQ/aQ096cco3UFUAZ6ZdXScAZEtqNBPL6ApqDsVMk+boQc5G0eZPZ/EPolmX4DOc6DzPPy6H0LuBUrByEMzz4Ffvn+FJacGcgxltes9v7EUFiFU6kyaQTvt4hCaCc23IV6tGXgUF2qTkH+tbN8fUbrzSFmjlHbYw9xcU8OsmW0NWenH7LnopxHUfF1HeAFQv6NCftp+SQf2i8fCoE1PubIFX+i3yiiyKTbnDMppvelI9TdGNNbJwFjP5LcG3lLVuQAiMgf4POCC/ATwzsvvFZuOcpkc7//3wyHeUUIzz5i5RGxHJLT0Forqt1lDCz6S+DoSGkR+uBcvPvoK1XUZquJ5lrTXsv3+nwQKeeAdILwqhDcovaFnDmgSkcI8OQu5V+2nQPL3+PED8KIfR8JroLGdIPWn0uuZ5yH3H7TzSqvuAIh+Cmm6GaJb26yZNKBBUMnaT/alAc4iZJIFdZdA3cXWsVm1W/Huwz6jxdB1me0T3+wE41+EZCtWvpiDjst7lU569nyF4Ug5edDFFG34wMxBkrdD9dftXIr6NnETFdOSfhBF4/EqkxPOv0eh05XMY2jbU2j8oKD5KgtchBIHiVmeOzwMzZvC+efmWg29v6BkalJxXmr7HYzI5hDdwcTDJIzUndtnEwnbRU6T95hDVny/MU09TTRjHeRXBd4rezwP+OQYH9MxAJ89Zneee/hFPM9DRNj2C8Obxfs9t0FHIDEgl8C0+5BQb7ONgVFVdNEhRechTT0I0x4atvb9l0+Os81Or6IKLz1Rz9rbrmf7yb6Kth2KhfIITLsTTT0alPoNeVbQdhjaeC0S2xapPw9NPQKUZsmaX2L6NQUyT+Dn/g/yrUGefR2oOQWWFETPwiDVEFqtdEEJbxw0/qR58qEqfnTsDWSzYY6+dB/2PXI+mv+oWDGjGlw0ysdYfRKE1rDPLjQzcEYqp4eSfHAo+H2ggF+O/elLZFNouhlN/9VSRuGN0PbTIfesHZ8QRD6BVB8NkY+ZmXe+vIQ0bWqaWt5PkbTa+LavQdNNA3a09kY7zg4kiIOZe+3pkPw95F6xDSKbFz+rgRDxkMarUL/NSiwHuChIZMOSteIUZ8IXXkVkNjAbYObMUXJsd/TLVntsxk//9UPmvvQus3bYcPjGJD23UqzeULFFuDK53iHRZBAYglvn/AeWOpC6Qd9WYKe9HwPfgt+Wu6QITVsAzLA7g0JumhDafQv03MTwZAEA0mjPHGtCAvDipVSItxKkHu77ls7LIRtU4+TetJLDuosh+Wf7XPwF1lUaWgOa70Wyz6OLj0IVfnTsGiS7AXJce8od7LjrO2bo3XwHEl4T8aajVFEK0jEkVIfUfBMAf9GhA5yHWFNSZENrUOr5aeVrRCg2HwVo/PPFWh6Jbo5Ey1y7EnujHS8F48iCnzRtGBGk5SH87tug64dBYI9BeNPgYli+1gD476FthyHT+29g60Pvi5x4wQUyIDt8rwnxRrcibDIz1vco7wPl92urBc8VUdXrVHVLVd2ypWVgHXTH6LDWrDX4zKHbL52peWQjSh2aPoQrzSNUffyOS/AX7ILffgqqvWaSEreFOiKYPO/qliMOeP3pNzlx+7M4dbfzmPffD/oeX0pSC55QWiQLTcdExLB/vVr6NiB5IKtSqr8vJwaRj9mvubmlfDOA/4FJD1dsvmsw0yzksLOQ/gt0nBXk1gtSAjnIv2d2hLFPQe23IbQWuWxlzXw+m7RcferBwHjDQ5rnWNAMz0Kab6msIffq+jk/LC1R802rRe8pFxiLw/T/IE2/oWI+JxEk/1E/n0dApFfndP4ttKOs8iR+oClAhje2807sb5Us0gCyZuV7/flBOePQSO33g+86ZjX+sd16bzCs/TgqGesg/wywnoisJdaBcTCwfOtyOvogtWdCfD/Ld9ZfZLf15aTuNdVDf54FrK6rK98vgjTdAtVHQfVspPnWYqomnUzzvd3P55V/vs4Lf32ZU3c7r+/xG35i+WBiUHM0El7Xnq/+FsS2s8AgEcujV+1h2xHGLgAe0Aax7ekT6CObItU2S6aiRjoEXhMVOi7hDZGGnyM1x2KLrQUUyAdlh2XbSwNkX0FV8aq/Tmj6gxxzxTeIRJVI1GffIxbS2JIDwmjydnT+JvgL94bQSnjT/oA37Y/gNeMvOhB/wXb43TcjdWdZioiozdwLF15VW0zU3ouMiudFTQMo/DEqAn2ZhrvmF6JlQV8is6z5qXhByUHyjlLTUPdVdueSe930bNq/A7rIcvn6Udn7BKI7lJmKFIar+B0/wv9oE/wFu6C5t23r6GbI9H8hLQ8hTbfghZqg4TJsciCg3dYb4Vgqxtz+T0T2Bq7AEoY3quqFA207lez/ViS061q06wqK6ZiqvfEarhjWexe+v4jD1zueTNC4JJ7wQGZO0Xt2yGNn30QXHUBFtUv9RTazXPKdoJEIW8iLf8EWZYtphSqk6eZiqkIz/7bzkAaoORrajgAyoHmk8efF6h3NzUOXnByU4mWDGWYtaPlNagiIQuLLeHWnFZ9d8sZW5DLdQYAXm7XnXrPjEIbEV/CC7lB/0aGQfQ67eFQhzXOKGu2qebRnDuTeROL7FWvB/c6fBOJrAnUX4CX2s+39NrTzx+AvQWqOK+7H774JOgNpgMQheEGtuGoGnb8VxTSd1CHTn0FE8Fv3LGvMqqKYQ++DBy1P4oUagn0qpB9EM09ZMxkpG2dkU7zm2/v/ftOPoe0nltJy/VgKOphY+z9VvQ+4b8gNHcsVmnoI7brGZGLrz0G8xoE3rtrbtMkDzRZJfGXYx2lepYn1tlibuS/+DwW2+dyWww7wQKABX759CpacBM13U1EhEt4Aqf2u2bZlHg9eS6GdlyPNvwFAop9Amn5V3JO2PGB116G1KhYPJbwaNP0K7f6VNQblF0C69wwzDySh52b87MtIzTFI7NPUrnYcdF4KErU7o8gWZVU/CuWpDX9B6RwkhOYXFIOzSAip7puj92pPRqtno/4CJP03NHkfVO2FeE1I/UUV26r6pmtfSD/1/A6tORokgbZ9k2LHb2h106opLJRHt4DkBxQlFEIrByJjPkiotAjrzUDKBMS06yrovhG7IBS+Gw1MxQcgMEMPHgR3dI6lYcIXXh3ji2ZfDgyhN0SqPtP/Nrl30PaTgRTkXkPbu5GmX/a7LWBlctP+YuWD4fUsCA4TEeHHj5zNk3c/RyQWYeu9l9KAIbqVpWsq2vEV8T+ExpvN6DnUgtSeYpotiYPQzNNYVYoX5LkHGJvXALEdBxh3FVJzFH5+PrRuP8gAfcg+jS7+D7Tcj1d9OBrb2fL/4Y1Al6CpOyH/oTUp1ZSZtNScAEvOwFIVKWifjR/bM1DWDKHpp9DOH4JUIfUXFtNYpo/zJVSDypvsq0jdKf2dhV1stFzQLIL2/MEuboXxR7eo7BpNHBaUjCaR6q8GJuP/QKXeHLR65oA3A+rOrKygSt1LSX6hIOgWqtC16TPCyMZo7Ql2d+KtgjRcMshnPTDqd9jCeGhVJLIJmn3d/m94DUjtd+y7nqKMebpmaXDpmrFFs6+jiw7CZlJVUHcGXqKvh6qm/26NLoVUh7cK3vS/jedQlwo/3w6tO1IKICGk5dF+y+1UfbTjLDR5NxJeA2m8fsiyvEGP3fFj6Lm+8smaH0DqVusrKMySpQZp+AUS61tBrOqj+UWmsJj5J8R2xqs/017LvWvfRaGMUBJI/U8g+im0ddtg1izgzcALqlg09ailkwrfX2h1vJZHeh0zadVIudcg9QjgQ91ZVgfffQ2lKheBqn3xGqx00++5HTrOtxl7aFWk+Y/FMkW/+1fQeRml7yEMdRfhJawSy2//XrB2kQZi0HgNEtmkYrY/Fqi/BF24j6V8NA+1p1o/gnbZGCOz8JpvG3I/yzODpWumbgeAoy+ZJ7HbZAWSgXlFP0Q+EVQ5JDAdlYFK98aGdDLNhYdcwZdnHs1Vx/2SfH5wSzwv1ADTH4fYnqZB3vT7AQN3qutdTvpsjr1W3YDZO61L+6LKxVj12/EXHYY/f0v8JWdZSmMwKurDgcj2eDWH4U271yRxiduPVENk4353IeJZN2lqjunTJ3+N32EzVgnPrGy5VzAhsSVlaQwFv7VkGB5ep2wRNmba7+VDVh9d+AXoutRm1149MuNZM/XovhHKyxi9JqSmzPmo6+cUdeXz76PpZ9H0P9D040E3cfnnkYOOU/FTfzN5hfpzIfFViO2KNF6PF/v0mAd4IHCR6g7y+inoLi+zzdkC8hTGpWtWJCKzqBDBivbflyZeDUy7x9r3Qysh0YENsMeCWy+6gyfufJpMKstfbv4b62+xDnseMbgLj+fVQ+NVg27jd1zKfVfdxZvPtqDqMe+/H/Lb827n+J+VWtq18xJzfCIHqbtMfTK+94D7lOrD0dTdQcojgjRYdZBqFjQE4fUsyCa+BKl70dDqpbr8cjK9zLvT/wBMP0jqTrcmJM1YOWtsFyBiAmJ+UBUT3qiYGpHwTGj6Jdp9M4TW6GNPp50/hvz/lX0wiyD/EUWphSINSMvjldUxoZWCY/p2Iem+Ac09b68VKqAqFmEV2mej3irItD/j1X13wM9yIDT3lmkUhdeE2J7DbqIrjXm1sote2L6T3GvB+kcIYpPD4WmkuCC/AiHRLcybMnUXRD6OJAaWexWvHuL7juPoSsz/X2ux2iabztI6r28Hq/pd1gUaXgsZhriUahJ6biCXaaIw4VXfJ5PuVcOdX0RJldKvlAPoBwmvAS2PQv4dW6D1qnntqTeZ9/yJbL/3XKKxPOTegNQdJhCGoLUn4vU2sY7tCT0/K3tcCjwSmQXTn7RGLW+aGW/4HdZ0VSD3SlBrbwFZolsFmjJPoa27WPlj7ffxEgdYy3/FSURN7ZGIfefJOwCB+gv6lD9Kw2Vo+3fsolB9JHT+kOIiav49SMyGnl/0/aD8hXbnmPjSoJ9nbzT3Hrroi7YmITGofq9y3WIYSGRjtO5su0sJr4XUX2BjTt5lDXkT9P98vHDpmhUMqdoZr+FyvOqvLTd6Ha899SZ3Xf0g775uJYhfOH5vqqpjJOriJGrj7HpYpROU5t5GW3dC2w5FWz9TUeM9MCEgzJ6HtDFjtQzRmE99Sx2HnnFAxVZScwxIwtIrXiNU7TXknsWrCXLL1WQzWU7b43xWXv19C/A2YkoCYklzhOqFV3cCeB8rPZG8raKpTCRqJtj5t/E7f4qmHqDyzzdKX4MS0PbjbKaundBxNuq3V+r8ANRfaQvXPTci4Q2g5RFk+tN48d37nmtoFbzm2/CmP4YkDqtsUJKE3TVUH0e/TWkjWdzMPhf84ltqLDWyQj0vcQBey714jT9DvAarNqr+GpLYf0DTkqnC1D47x3LPk/c8xwUHX2ZNQ57HVU9cyMe2XIebXr+Sd16Zx3qbm29rOdp9owUtFDSN9vwRqf1W/wcIEImi9ZdRK6dz3eMf0p46k8bVP0s4UvknINHNoOWvkHsPIusP6y6hnJ6OJJlUlr/+qZG1N0oSiytIBikGZM+qanqhmjFZ3+ITbSZ5XLVT6an8h+iiA4NKoiqr/Mk8CYSQ+kv6v2hXrBn4aPqZwCC7QMhKJzteD0o2Q5B+2ITYhkDEQxuuhI4LQWJI/bmIhJHaE/DjB9pCcupBIGs9Cr07WIdDeKOytYeqQFCtfzR5D5p5Bqn6DBLraxE5Gmj2P9YfEd0KCa8zJscYbVyQd0wof7n5UdI91knphTyevOd51pq1BtNWbWbaqgMoVXoN2H/dwP1nmIt3Xnw3iO+GBwwmoCFeE0RHpn1S11zLZjtvwsN/CHPAUQtZaWY6mNMWApVUyEJo5gXIPoeGNrDXyhc9e4vAFcsag4Xz3P/hzXh+8AHVnBiImgVm4x1nUym/mzcnruL4spD5F/7ib0NkA6T6G31SNqWxPwPtx2Cz9EbT6ymcpVeDZv5u+xcJ0mqe3YUl/4yEVoP4ASbdPAgSWR8ar0WTJhU9kCSw33MHdJwDJNHkHdB4LRLbZvDPZinR9N/RxcFkQgSabkMiGwz+puUAF+QdE8p6W67D0w/8m3RPhmhVhDU3GVqaVqqPsXr/7EsQ/TQa3x/tuh7ybyPxgyrFtvpBc+9Zjje87tIv4g01NhEuuPs0nn3wBRpXOhGR+b22yJs+PeCnn4DFR1O0BSxPcYS3xov0mvGHN+w1qx1a0FXi+wTdyBk7ti7E0jrlgb68gihkj9P3Q/pR1F+ClHXslqOdPy3dKeR9WxxNBOmvzLNBiWKPXV96fotW7R3k13tQYtat2489YJ9ziG0zdMDOPE6psieNZp4d/SCfLHTpAurZ2oYL8g7H4Bz03X3JJDO89Pir7Hzwp9nmc/2W+lYgXk1FZ6rfcUGQ506hyfth2p0Vdn3l+N03QOcVgNjiZsMVSxXo1e8CTQ+qhx8Kh/jkPlvgJ39g3beB1Z9FuzjEdkPTf4fFR1ESO4OKWXzuJTQ3r6KxTMJrQNNNJmcQXhupPnLoAXvNtsagWcAzcbjQGpB5KVDdXBCMIQaxnWxxN/sCFsxSZnIy4Ik2QzbQohepzLmHZ1ZWtITWgezLlO5WUpB+FBg6yA+L6E5BvX8KiI1NRVh4E+BR7GISsyqdSYAL8o4JJRQKcfi5S1dxUY7poZQ7NWXRzsuh/odWCtqbzisplvil/2oVIQPonWt+Edp9DaiP1MxG009CxxmAovED8erPGXRsUrUr2vOJIGiGTCKi9gSI7Yku2JrKAN+bnC0s+0kQhdrv4iWCu5TwurbI2c+CoapfkuSNbGldvs23mvmJxK27s6Bf7/eY1ED+XVuEjG5tzVeL9rWZqsSgap+Bz6/2TDT/AeTeNunpWMk0XMLroPWXWF5eElDzbQg1ULqDGN6dyHDxEp9HvapgBr9Tv01ny4pUH4Fqt62DVO01sjWGCcB1vDomLaqKth8D6cepDJgRiGyC13wbqlm0+4ZAyOsgdMl3ykxFYsj0x/rVHvf9DCzYFujAOkpXDrTmu0vvnXbfoM5H6rehC7anaDOHh8x4CZEo/kez6Ov1Kti8qzDzLycG0x6Ejh9YTb3EkaZfI70arPz2k+ziBRDbBa/hsgHHN+C4i3Xpa0Ns9xGntDTzgtX344Eo0jQHyNqdSGgmUn2EXYQcy8yECpQ5HGNG7i1I/4u+M+JscZFSO38cKE+m0NRDUHehdXpqEmpPrwjwqhqkJ7KQfgIL8GAdpfOBXncGAyxIll6vsdlwUXQshC46EE0cSWUeHIjuAQ2XIrlX0PaTwH+/984svVEwLNFOtON8058HNP03tP27VPjLpu5H/fMRr7p0fjBk0JbwulCzbvGx+l3BncPSldxq8nZKZjOgqXvwak9B6mcN+j7H6OKCvGPy4tVQGSw9bDbslSQEMk9RTOWIZ0Ye0x/rd3fa8f2gNR9KZiQB0gD1l8CSE0DTUHN8Ke2R/whtO8IaoiIfh+pvIbHtbJbaeBPacZ45Y2mPdVp2nElpdh+Q+Ssk/2iaMNFPW7cteYr+qrHtTe2x4sbbgq6qjy4+oXSexTEHnq2A3/WLIHUSRxuuQLTb5Jejnxow6Kum7byyzwNRVEJW4dJ49fCcl0Lr2NhJBZ/nEP6sjjHBBXnHpEVCK6O1p5vYlNRC3dkW1KWqtChZtQd0vUMxNRLpv/JGNR90ehYuGoGIFnlAoPE6vOgsNPY8oBWzWu282AI8eQuI7cegVXsiDZci0Y8j0/6I37oH5DsKAwctt/kDyELXFZbzJYOVJa4C8QORql0tD4+Pxra1dIzUIXU/CN6bD95T8ekgjTeaWmXuPei6msIdAItnoxIDFOKHInWn9v8BJ+8NNPNNlhkFsi+hHT8alhqkVH8Vzb9jpuEodF+HH14LL/7ZId/rGD1ckHdMarzqQ6D6kNITZc1DYOWWhNZAc3OR+F6D5NA9m61rW/DGGDRci+TfhuinrbKFQqqj18zX76Eyh56B1D2oXlSqMU8cbi5KIphVYSKobMkG+4sGTU6FYO2b01b3z1GSSGwHJLol0nh10AkbK2nVSARNfBV6bi4NIbS6NXYVxlMx5jxFaebkHBgoyPdLzjpoh4FIGKJbosm7MGlnTDJ4lIK8asY6etP/sDuMphuct2s/LB997Q7HGCEiSHwfvNrjy/TWB9iu6SYIzwpSEr/Ei30SSRxSDPADvrf2O/TxH5V6yudQXvUhSNOvofYCIB/k3AspmzJZ3z5kTARs8ZH4XdcHY63qk2Lx6r4P1ScDtSZK1lBm5h1aOxBZi9JnXhcaRPs/vk/g7+vZj8QtN19z3MDv6U1otbLzi/TxB14mkn+C9JOYkuQbVkHk6IOrrnE4RgHVJJp6HLp+AV4EqTsPiWzYz3Z5dP7G9Fl47dOg1A/eSkXN+BGNMb8A7bwo0H9RQKDmJCTxZbTnd/Zc1f7Qc4ulnaKfQmqOBM3b3UPP7RBeE6laOiVIv/sW6PkNhNdH6i9AvNEx5NbuG9HOyyjeqVR9Fq/hJ6Oy78mGq65xOMYYkTgS3wPie/R5TTUTrBXUItHN0MRhFjDJYYE9ROVCrAeJb5qoWfJ3FP1fB2jwGgjNvY123wReI1J9FBKaHsypCw1JMZBqtO2QwOAE6LoOq4hRc7RKPwxNt0HbQeB/AKpo9vlip6qm/2F18rEdkfCa/Y6jT0pttIjvBz2/NeVQiQYm647euCDvWCZUNSjr06D5ZvnMAKraLT3eNCQ0YxyPm0fbvmLHRtHE4UjtGdY8pFnTne+6stTABJjl3mZ4VZ9BY1ugXVdbZY3XjL/w81C1N1I9e9DZtPqd6KIvBUJuETT7MtJ0E1JzIpp5EvxOu2jE9oDOCyndRfSq+sm9Adn/BAG+kMf/A9Sdgd89Bzovsvd2XQHNdw3aNzDaiNdovQP5DyA0AwkqiRyVuCDvWCZ0yfcg/Rd7EN0BGcK4YyJQzaCLDob8XGu1b7gMqRqnbsXcXHMeKmi89PwaqT0pMHAJ5tT6DbT9OUq56xASbC9VuyNVu+O3n27BFd+qhcIzB5dBzr9LSUohE3TdFvTv/w7aAWLm7BpaDfKFuvwQFU1aXqOZdRTH5kEo6BBO/YlSHXzIPFTHMciDLTozxJrJis7yOe1yTApU01bPrT32k37YzCyWNzJPmROS9gBptPPS8Tt2qJmiSwkSuCeVUPXR9KPYfCuospGaipZ/zb0DqfLyziTk3hniuGsGNfIhIGa194VRSNj01EWCBec5Zs6dOBSmPQCJYyG0LsQ+gzTdhuTeMHMNYhDeDGm8xnYU+QRWBw+gEP4YjuUPN5N3LAMRM9fQTnsoMVhK/fVxwasvE8vCZqdlqKp1Z6Yfh9hOSPyAYS0squrQ3aNek7lxdV4CXh1S/6PKDVL3B41PgYBYeBZUHwrZF1FvB2uoyv0Xq4wp1NULDHEnIl41NN+B9tyOeA2DOjJJqBmp+37piboTgRODc0yaCXbh2Pm54FkTmNSejBKF3KsQPwiJVnrJlqN+h5U7Zl+xi0f9RUPKDDtGBxfkHSNGxIPGG9GOMwHfKkqGavVfCjT3ngmEEUNqjxt5DbTfSUn6IAF1vQPtPWZ8QRLSfzdBrUF8XQH8rquh62eoVCONVwNhtP0Eu1uoPRWvLKhK1c5I1QA+ov7iMvlgH/wPoeNMS46E14emOYGxeqSkJBk/eNBy0OJxQyv18XddavxuKiqBtCN4HDK3qrqTh7Ub7boSMs8BWTMSiX0K4vsv29gcw8IFeccyYR2dd4/6flWzaNuXwG8DPDT7FDLt3pHtq+M8ikFeQLQVWLP0euZZSlrkSaseGSTIa+5dK5UkB7rEtGbwS56rHRegsZ2R3qYf/RHfG7qvC7TX/UAjJ7jryL4O+XlmzN18hwXH8GrmBzteeM0mQZz5B9YhO7TRR79k/0dpUTdrFzfHuOCCvGP5xF8UzMB9+8m9hao/suqdcqVDVXrr0kjVbuYmRAaIIrHPDLHDXh2kmqFSVkBKlSiDoJlnrHKl4ae2t/DqZu+Xn0exjj24e5HwTKj55pD7HPR4qqahIxEktOqw3iMi0HAVZP9tdxPhpRcXs8qmF8ue8dHYvv22fjlGHxfkHcsnXouVDebfxwTHNh12gPeTD0L6YYh+0vLr9T9C244EbTfnosimFdtLbDtousHcjKJbIdEhjEtC65gmTup+QKDuBzYz7bzYHsd2qbDC63+MD8GSU7A7jDA032rpqMYb0I6zwO9G6k7rXxN/GKj6JpvgNSJiwmDacSYk7wYUrTkWr+aYYe1LxIPoFiMaR3DgXhc9RUIDWzaq3wapB4K7iMGljjX3NuTnQ3RzV0I5AK7j1bHcon57UDYYReNf5Prv/YHHbnuC9bdah+/96jjiNX0XeTX9OLr4OGyhMA51Z+ElvmivjfROYKDx5T+yNn+vrvRYeyC01pALsv7i40qlpwgkjsRbKg2ZQcalKXTRobZgK1Gk6TfgTUdbd6J0xxFGZrw8Ln0Nqmq2f7m37InIpnjNv+l/W78bXbhHoN3vQeIgvAEsAv2eP0LHuSb45s1Apt25wgb6wTpeXQmlY7lFvAYzkq7+Kn//wwvcc81faJ23iKfv+zc3nTWn3/do5t+UqlCS5uJT2N8oBzQJrVQM8MXH4bWH1/If3RwoLFIr9PwOzbeOzsBSD0L+bSBtuvOdF6OFhrXiYKvoXytn9BERpPm3SN0ZSN0PkKYbBt4491qx1BWSwZ3HAHRfjaljdttaRuZpNPVX/LbD8TsuQAu9CSs4Lsg7JgUL328jn7MFyWw6y/x3+g+IEtuWUu12FcR2HZ8BlqGqaPZVNPuy/d7P3bIkvhbUnhee8Hp1vS4DFW5LnnmrLimYcces67fh58tsYq7pv+G3onGCJgAADHxJREFU7oLfuhea/c/gQ5I4kjgISRwwuBtUaCal7tsIRAapvfdWphjCNG92hu0nWlNWz23oknOX5nSmLGMW5EXkHBF5X0ReCH4Gr0lzOAZhx4O2IV4bJ1EXp6o6xoEnf67f7SS6lc0Uq49FGn+KFx/HSpQA7TwfXfRl+5m/KTp/Q/wlZ1YEexEPYjtSNNLQvDUgjQax3SC6HbZwO81KL7UHq27Jgt+JLjnL/FlHiPpdZlSSnwf5t9G2ZVsULiCh6UjjLyG6PcQ/jzQM3EEtDZeaP4C3MtSehpC31A0Aaci9PCpjmuyMWU5eRM4BulR12O2FLifvGIyOtk7efG4uq39sFabPbJno4fSLagadvyl9VCYlgTRcW2EwrZo00/HcW0jiKwPX0i/l8QFEosHi62K0dWf6uEYBhNbCa3mw9F5/Cbrk+yY4lviqCYsNdJz8R2jrrpRy/CFkxqvLfHewLGi+FV24lzl3iQc138ar/vqEjWc8cSqUjilBXVMtW+w2SFel5kBTI65IGR3C1vWr3f28VuneJBKv7DRdRvzuX0PnjwBB687GSxxksgoNV5rXrd9KhQesX5ny0o6zIf03IAudF6ORjcqMR3rhzYDYDmYqrgqJQyc0wANIqAWm3W3OWaGZSGz7CR3P8sJY5+SPE5GXRORGEWnsbwMRmS0iz4rIs62to7Tw5Fjh0MyL6IKt0QVb4S8+3maxE4CIhzReZ7llbxqWjoma92t0mzE7rvo9QQlnDshCx7l20cM6br2W+8yjtnyxNd5L6iD3DqWGJQnq9ftHRJCGnyGNNyHNt45aZdCyIqGVkcShLsCXsUzpGhF5GFipn5fOAJ4EFmJL+ucDK6vqoPdOLl3jGCn+ws9bZQYEqZGrg0XY0Uezr6Htp9hsvfYMvPjAOjKqSSsH9GZUzHRVfUjeiebnIfHPgTfDFD2zL0LVXkjtaUs1M1a/B12wFaUgHUFmvGgWfGX46SesYiW2DV5838rXknfDkjMsry0JZNq9pnvjWO4Zs3SNqg6rdEFErgfuWZZjORyD0/umdOxSB7r4aNOYAVhyEhp73LTN+0EkDqF+6vk7L4aeOUAa7bkJqj4L6UeBjD0fmbVUXqjiJdDa70JnMFuv+0GfAA/gxbaFAS5+XvxzaHg9yL8H0a0Rb+CGJcfkYcxy8iKysqoGfwnsB7ilbseYIfXnom1HWBVJbOcKqd5Rx28rP3IwU+8/yBdQVbTnV5B6BGLbQfoRSno5Atk3KOXsc4GGzdLhVX8NTRwMSLHLdWmRyAYQ2WBE73Usn4zlwuslIrIZlq55BzhqDI/lWMGRyKYw/RkgbbPnsaT6G9B9g1VwRDYfUsIAgNS90HW5mYfkXoLQBsB8rOknD9Vfh47vYfrvIajaZ0RDK+/41PwCk/aNbIiE+suqOlYExizIq+pXxmrfDkd/WEfr2OvZe7XfRqv2sLuGyMeHpz2ffa3kDqVJCK8NVTtC7n9I4hAk+nE0+nErX4xsssy5cM29ZWJnCOBD0y1IZKNl2ufyjKpv6xkSRSIbT/RwlitcCaVjQlG/2xb5Jrj8bmmRpUhpqKYgsiHFxicEie+LxCqrbSS0somyjQLa86egAUqDx7ch9VOzA1RV0fbjIfNPUEUTX8IbxdLUyY4L8o4JQf0etO1w60r0pkPzHAtyUwzNf4Au3B9ImV5M4mtI1Y5IZJMxPa6EV0OJ2XGpgtD4eq+OK34rpB+juKbR82u09rTl1lR+vHGfgmNiSP7JDK7Jgz8f7bpiokc0Jmj3b0ziWHvMJlG7xjzAAxA/yJyXvFUh/lmk+qtjf0wCFcmeP6Kp+9Fyy8WxRKqpCGVS5wJ8GW4m75ggdNCHUwavBvszy9i/Xu24HFYkjNSfMy7HKqCaC0xP3gcRiD2MNPxkzI8rXjU0/hztOAeIIg0Xj/kxJxPucueYGOL7Q3hdLPC1ILUnTPSIxgRJHBEYbkQhuoWpT05V8u8FJi9Ju3NJPTAmh9HcXPyFX8BfsCN+z58BkNj2eC2P4LXcb5VWjiJuJu+YEMSrhuY/mbepVE/Z22vxEkjTryZ6GOOD12JlpQrgDa+0dATo4m9Bfi6gZnoe2woJrTImx5oKTM2/LMekQEQQr3bKBvgVDfFqkKZfm8xxbHek6caxOZBfUEvBJBgqmtMcvXEzeYdjBUPzHwH+mMx+JTJr7IJ7gZpjofNyC/Dh9SHsOnQHwwV5h2MFwu+6Brp+Bgia+PKkrCf3qo9Ao9tZ1VJks341ehwl3H2ywzHJ0czT+K274rfuiqafGng7zUPXlVilT9p8Zf32cRvnaCKR9cwFTCJDb7yC44K8wzGJUc2hi2dD/l3Iv4u2H1XUke+LB5T7qwolM3HHVMUFeYdjMqMZs7srPk7bc/0gIkjjlSD1IDVQf5FVOTmmNC6Z5XBMYsRLoPH9TOVSgfjeiJcYePvYTsiMZ8ZvgI4JxwV5h2OSI3UXQuJgexCeNbGDcSx3uCDvcExyRARcl6djAFxO3uFwOKYwLsg7HA7HFMYFeceoovlW/IVfxJ+/Of6Sc1CdqvKSDsfkwAV5x6iiHedD7hUTHkvdCelHJ3pIDscKjQvyjtHFXwwEZhGq1nrucDgmDBfkHaOK1J4AEje3ntB0iO0+0UNyOFZoXAmlY1SR6FbQ8ijkP4TweohEh36TY0Ro+jG08xKQeqT+IiQ8NvrtjsmNC/KOUUe8JvCaJnoYUxr129DFx2NG3YIuPgppGRsnJsfkxqVrHI7JSH6h+agCoOB/NKHDcSy/uCDvcExGwutAaF1b+yAO8UMnekSO5RSXrnE4JiEiIWi+FdL/BK8OiW4x5Hs0/yFIFeI1jsMIHcsLLsg7HJMUkShU7TzkdqqKLjnNlCoBrTsPL7H/WA/PsZzg0jUOx1Qn/z9I3Y85QmWg88KJHpFjHHFB3uGY6kgME5svPK6asKE4xp9lCvIicqCIvCIivohs2eu100XkLRF5Q0T2WLZhOhyOkSKhlaH2O0AUpAFpuHyih+QYR5Y1J/8ysD9wbfmTIrIRcDCwMbAK8LCIrK+q+WU8nsPhGAFe9deh+usTPQzHBLBMM3lVfU1V3+jnpc8Dc1Q1rar/B7wFbL0sx3I4HA7H0jNWOflVgffKHs8LnuuDiMwWkWdF5NnW1tYxGo7D4XCsmAyZrhGRh4GV+nnpDFX987IOQFWvA64D2HLLLZ34uMPhcIwiQwZ5Vd11BPt9H1i97PFqwXMOh8PhGEfGKl1zF3CwiMREZC1gPeDpMTqWw+FwOAZgWUso9xORecA2wL0i8iCAqr4C/B54FXgA+JarrHE4HI7xZ5lKKFX1DuCOAV67EHCtdQ6HwzGByPJktCwircD/JnocI2AasHCiBzFBrKjnvqKeN7hzXx7PfQ1VbenvheUqyE9WRORZVd1y6C2nHivqua+o5w3u3CfbuTvtGofD4ZjCuCDvcDgcUxgX5EeH6yZ6ABPIinruK+p5gzv3SYXLyTscDscUxs3kHQ6HYwrjgrzD4XBMYVyQHyEDGaaIyJoikhSRF4KfayZynGOBM4sxROQcEXm/7Lvee6LHNNaIyJ7Bd/uWiJw20eMZL0TkHRH5T/A9PzvR41kanJH3yOnXMCXgbVXdbJzHM544s5gSl6vqpRM9iPFARELAz4HdMPnwZ0TkLlV9dWJHNm7srKrLYyPUoLiZ/AgZxDBlyuPMYlZYtgbeUtW5qpoB5mDfuWM5xgX5sWEtEfm3iDwmIttP9GDGkWGbxUwhjhORl0TkRhFpnOjBjDEr4vdbQIG/iMhzIjJ7ogezNLh0zSCM0DDlQ2Cmqi4SkS2AO0VkY1XtGLOBjgFjbRYzWRjscwB+AZyPBYDzgZ8Azkh1arKdqr4vItOBh0TkdVV9fKIHNRxckB+EkRimqGoaSAe/PycibwPrA5NqscaZxRjD/RxE5HrgnjEezkQz5b7f4aKq7wf/LhCRO7DU1aQI8i5dM8qISEuwQIWIrI0Zpsyd2FGNGyuUWYyIrFz2cD9sQXoq8wywnoisJSJRbJH9rgke05gjItUiUlv4HdidSfRdu5n8CBGR/YCfAi2YYcoLqroHsANwnohkAR84WlXbJnCoo85A566qr4hIwSwmx9Q3i7lERDbD0jXvAEdN7HDGFlXNichxwINACLgxMAia6swA7hARsJh5i6o+MLFDGj5O1sDhcDimMC5d43A4HFMYF+QdDodjCuOCvMPhcExhXJB3OByOKYwL8g6HwzGFcUHe4XA4pjAuyDscDscU5v8BVugfitDuNXwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4b8gGV0YOxk",
        "outputId": "36636253-1cc8-4392-8fa1-753b595cfd5e"
      },
      "source": [
        "Logistic_lr = LogisticRegression()\n",
        "Logistic_lr.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTY_wUTkaD_H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37d9e937-8943-4c39-da9f-6ea3545634a4"
      },
      "source": [
        "h = .02\n",
        "x_min,x_max = X[:,0].min() - .5,X[:,0].max() + .5\n",
        "y_min,y_max = X[:,1].min() - .5,X[:,1].max() + .5\n",
        "# e.g. arange(1,8,1) -->[1,2,3,4,5,6,7]\n",
        "# meshgrid e.g. meshgrid([1],[0]) --> array([[1]]),array([[0]]) \n",
        "xx,yy = np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))\n",
        "print(xx.shape)\n",
        "print(yy.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1337, 1263)\n",
            "(1337, 1263)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjB78IA3c9wn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b760974b-43bc-4f5b-f5ff-d39116d40712"
      },
      "source": [
        "# ravel() the result of it is turn every vector into 1-dim\n",
        "# e.g. [[1,2,3],[4,5,6]]-->[1,2,3,4,5,6]\n",
        "# np.c_[c,d] concatenate c & d by rows\n",
        "temp = np.c_[xx.ravel(),yy.ravel()]\n",
        "Z = Logistic_lr.predict(temp)\n",
        "Z.shape\n",
        "temp.shape\n",
        "# xx.ravel() --> 1040 * 1597 = 1660880"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1688631, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bzCD7tEadP9J",
        "outputId": "5cf4dcc0-a743-4e5e-bd50-d097cc1d2ab8"
      },
      "source": [
        "Z = Z.reshape(xx.shape)\n",
        "# category graphs three kinds-xx,yy,Z(line)\n",
        "# cmap-automatically choose colors\n",
        "plt.pcolormesh(xx,yy,Z,cmap = plt.cm.Paired)\n",
        "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMJjZuAtV1WX"
      },
      "source": [
        "\n",
        "**Online Learning Models** \n",
        "* Stochastic Gradient Descent & Passive Aggrasive Algorithms\n",
        "* Simple & Efficient to fit linear models\n",
        "Useful where number of samples is very large ( Scale of 10^5 )\n",
        "* Supports partial_fit for out-of-core learning\n",
        "* Both the algorithms support regression & classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0sPgGdQdlWn"
      },
      "source": [
        "from sklearn.datasets import make_classification,make_regression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0U3jBcsRWZ76"
      },
      "source": [
        "X,y = make_classification(n_classes=2,n_features=10,n_samples=10000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iRBLwgWWhSu"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GUz4CodWlNF"
      },
      "source": [
        "trainX,testX,trainY,testY = train_test_split(X,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TicYdDyrWtZj"
      },
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFQSpW1ZWygR"
      },
      "source": [
        "sgd = SGDClassifier(n_iter_no_change=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYPg8n7wW7uI",
        "outputId": "0e3e649f-3b8a-4485-b415-f2bccb7625b8"
      },
      "source": [
        "sgd.partial_fit(trainX[:1500],trainY[:1500],classes = [0,1])\n",
        "sgd.score(testX,testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.894"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGgkcC-UXzUJ",
        "outputId": "75aa4002-cb84-4962-9342-2cb572222234"
      },
      "source": [
        "sgd.partial_fit(trainX[1500:5000],trainY[1500:5000])\n",
        "sgd.score(testX,testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8104"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCZvw29PYgAa"
      },
      "source": [
        "\n",
        "**Robust Regression**\n",
        "* Robust regression is interested in fitting a regression model in the presence of corrupt data: either outliers, or error in the model.\n",
        "* Three techniques supported by scikit - RANSAC, Theil Sen and HuberRegressor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYoi6ZzdYrPU"
      },
      "source": [
        "**Comparisions RANSAC, Theil Sen HuberRegressor**\n",
        "\n",
        "* HuberRegressor should be faster than RANSAC\n",
        "* Theil Sen and RANSAC are unlikely to be as robust as HuberRegressor for the default parameters.\n",
        "* RANSAC will deal better with large outliers in the y direction\n",
        "* RANSAC is faster than Theil Sen and scales much better with the number of samples\n",
        "* RANSAC is a good default option"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eIPCAJ1YJj1"
      },
      "source": [
        "n_samples = 1000\n",
        "n_outliers = 50\n",
        "X,y,coef = make_regression(n_samples=n_samples,n_features=1,n_informative=1,noise = 10,coef = True,random_state=0)\n",
        "\n",
        "# Add outlier data\n",
        "np.random.seed(0)\n",
        "X[:n_outliers] = 3 + 0.5 * np.random.normal(size = (n_outliers,1))\n",
        "y[:n_outliers] = -3 + 10 * np.random.normal(size = n_outliers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZFdqpchZnO8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression,RANSACRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHZrZV_Bjm9c",
        "outputId": "9013776a-16ae-4d7f-8b7f-bb9925b1c6b3"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X,y)\n",
        "ransac = RANSACRegressor()\n",
        "ransac.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,\n",
              "                loss='absolute_loss', max_skips=inf, max_trials=100,\n",
              "                min_samples=None, random_state=None, residual_threshold=None,\n",
              "                stop_n_inliers=inf, stop_probability=0.99, stop_score=inf)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "9b190pmXpPti",
        "outputId": "e533703a-6691-4f11-fa4d-9b8d6281252c"
      },
      "source": [
        "plt.scatter(X,y,s=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f341a390790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD4CAYAAAAEhuazAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxc1ZXg8d95pc3aJVs2tiVZTLPFgLFk4Q1IOizdpDuBTiY0tkmmkwDGCZhkkpkOdGcmnc8kDST9SScs6diQdKfBy6RDp7PBJGwdAvEmyQtgAnHAkmWwLVtbSbJKqnp3/qh6z69KVVqskqpKdb6fjz+4Xm1Xxj5169xzzxVjDEoppbKLleoBKKWUmn4a/JVSKgtp8FdKqSykwV8ppbKQBn+llMpCOakewHjMmTPH1NXVpXoYSimVUZqbm08aY6ri3ZcRwb+uro6mpqZUD0MppTKKiLQmuk/TPkoplYU0+CulVBbS4K+UUllIg79SSmUhDf5KKZWFNPgrpVQW0uCvlMo4tm3o8AfQrsRnLyPq/JVSymHbhrWP7qS5tYtliyrYdvtKLEtSPayMozN/pVRGOdU/RHNrF0Hb0Nzaxan+oVQPKSNp8FdKZQQn1TO7KJdliyrIsYRliyqYU5yX6qFlJE37KKXSXmyqZ8utK+g6Pcyc4jxENOVzNjT4K6XSXmyqp+v0MFUl+akeVkbTtI9SKu3NKc7TVE+S6cxfKZWWbNtwqn/ITe1su31l1G01ORr8lVJpJ1E5p6Z6kkfTPkqptKPlnFNPg79SKu1ojn/qadpHKTVtYvP4iWiOf+pNeuYvIjUi8oKIHBSR10Tks5HrlSLyjIj8PvLfish1EZEHReSQiBwQkYbJjkEplf6cPP6q+55jzeadBIP2qP15nBy/Bv6pkYy0TxD4gjFmMbASuFNEFgP3AM8ZY84HnovcBvgAcH7k13rgn5IwBqVUmoltvubN4zcd7uSmzTvcDwLbNtqsbZpNOvgbY941xrREfu8HXgcWAjcCP4g87AfAX0R+fyPwryZsJ1AuIvMnOw6lVPqIneXbtonK4y+pLudAe4+7oNvRFxjxeDW1krrgKyJ1QD2wC5hnjHk3ctcxYF7k9wuBI56ntUeuxb7WehFpEpGmjo6OZA5TKTXF4lXrOHn8l794NTkWhCIBvqG2AgGt7plmSQv+IlIMPAl8zhjT673PhL/HTeij3Biz2RjTaIxprKqqStYwlVLTIFG1jmUJliXsPdIDgE/g4XX1VJXka3XPNEtKtY+I5BIO/FuMMf8euXxcROYbY96NpHVORK4fBWo8T6+OXFNKzRCJqnVs22CMob6mnJYj3SxbVOEu6mp1z/RKRrWPAN8DXjfGfNNz10+Bv4r8/q+An3iu/7dI1c9KoMeTHlJKzRDeah3bNhzvHWTNoztZdf/z/O6YH2MbMAZnfVere6ZXMmb+VwAfB14RkX2Ra38D3A/8UERuBVqBv4zc9xTwZ8AhYAD4ZBLGoJSaYmPV6Ce631n8bTrcSSgS6P2BIAAtbd2c6h/Stg0pMOngb4x5CUj0UX1NnMcb4M7Jvq9SavqMdXSiN8BfVlPOv92xCp8vnFhwFn+dwO+zhMI8HwOBoOb3U0h3+CqlxhSvesc7W+/oC7Dn7U5swrP5j27awZMbVgNgjKGhtpyWtm4aast5eF0Ds4vy6BzQw1hSSYO/UmpMTvWOM/P3ztZt27Bx215sz+MPtPfQ4Q+wcdtemtu6WFZbzsv3XM1cT05fUz2ppcFfKTWm0apxTvUP0dLa5d72WULjogqMMew+3AnA7sNdGNvoLD+NaFdPpdS4xKvGcUo3G2rLybGE5XUV7Ljnaravj14TAPj0lhbduZtGdOavlBpTvEoe7yJwQ20Fv/ni+/HJmQ+IqpJ86mvK3A1dB472aGVPGtGZv1JqVPH69ASDNrveOkWTswjc1sWdW1pYff/z7mNEhB9tWE19TTk+3bmbdnTmr5QaVYc/QFNrF6FIpc9x/yB/8o8v4h8M4pNwjn9JdRn7j3QTMkRVA4kIuT4Bc2ZDl6b904PO/JVSLm9bZds2HO8Z5K6tzW4TtksXltHVP4R/MLxJK2TgiU8t58kNq2isqxzRm+dU/xAtbeEPBWdDl0oPOvNXSgGxOfxyQGhq7cS7Rru/vZsv/+RVSvJz8AeClBTksOK/VGJZVtxqoNFKRFVqafBXKss5i7nGmKiNXAaILc6xDew90sNvvvh+egaGuWBeMZYVTiA41UBe2rAtfWnwVyqLRc32F1W4O3Hra8o4eMxPfyA04jkNteWcU1rA/LJZ43qPeB8KKvU0+CuVxbxtG1pau3j5i1djWYIxhpV//9yIx/ss4eF1DTqDnwF0wVepLBZ76Mrc0nyqSvKZXZTHZTVl7uOK831YAksWlmKM0XN2ZwDJhP+JjY2NpqmpKdXDUGrGsW1DR18Agaje+2sf3emWd0K4be/iBSW89o4fgOV1FWxfv2rELl6VXkSk2RjTGO8+TfsolaViq3seWtvA3NL8My2YPau9BtzAD8Tt7Kkyi6Z9lJrBvHX7sbz5/t2Hu1h1/3PcvGkHlYW5birI2Z0bq6G2XNM/GU5n/krNUPEOYAHcsksn3+/04bdNuPvmyf4htzxzdlEuax/d5fbveXDNUhC4e/s+Vt//fNyDXVRm0OCv1AwVewBLR1+Au7ftjfow2Hb7St441ssHHnzJfZ4QXZ4ZW6ff4Q/QMsrBLiozaNpHqRkqtpJHYMRpXJYlXDCvxE3vLD+3ckQgj23lHPu6ums3M+nMX6kZKnZ3rW0btwHbskUVVBbmcrx3kI3b9nKgvZsl1WU8tGbphF9Xa/4zkwZ/pTKc056hsjA34bm4Q0MhPrp5Bwff9bOkppwnPrWcdY/tcvP9EG7bsPqBF2gcRx5fd+1mPg3+SmUwtyb/cCeF+TkMDIXc4G3bhr/cvIOWtm6EcLkmhM/X/cPJ/qjA7whpHj9raM5fqQx2qn+IpsOdhAz4B4OEbEPT4U5O9A7y0U2/paWtGzgT+AEWzy/hvKoiZuVF//OvrynXPH4W0Zm/UhmssjCXwrxwe2VHyMCGJ5rZ194z4vGFeRb/8ZnVdA4EOT0cPe//6ocvoao4f8Q5vWpm0pm/UhnEtg3vdp/m9Xd6sG2bzoFhBoZHdt48cHRk4F98ThHPf/59WJbFnOI8GhdVuPcV5/u44eGXuePxZj1kPUtobx+lMoRtG27etIM9rV0AlOTn0PKla1n32E72tIbTO8X5OZweDnHpglJyfZb7WACfACJRawJvnvBjjOHPH3rZfVx9TTlPfnq1btyaAbS3j1IzwKn+IZrbzgRzfyDImyf6eP1YuOdOUZ6PC6oKaWnvZV97DxfPL4l6fsgAZuSGr0sWlkY9bn97ty74ZgFN+yiVISoLc1my0NNmOc/Hqf5B+iIHrvQPhWhp73Xvf+1d/4jXAGiojd7w9crRXpZWn/kAaKyr1AXfLKAzf6UygG0b1j22i1eO9nDZwhK+8KcX8tDzf+C/fT86Hfqec4p4/Vh/1DVLzhzH6BN4eF09VSX5UWfrbr1tBSf7h6JaO6uZTYO/UmnG2bTl3azltlk2sP+on0/8c9OI83Vn5ciIwL+0upRNH29k47a9tLSFd/Y6wT12l+680oLp+hFVGtDgr1QaideJ07IkbgfOWKeDIy/m5vioKilg+/pVIz5QdJdudktKzl9Evi8iJ0TkVc+1ShF5RkR+H/lvReS6iMiDInJIRA6ISEMyxqDUTOBs2grahj1vd/LmcT+hkM3JviGe+NRylniOVoRwB04Iz/rj2dvW7TZw03SO8krWzP9fgIeBf/Vcuwd4zhhzv4jcE7n9ReADwPmRXyuAf4r8V6msV16Qw6w8H32BECLw5w+9RGGej/5AkIsXlPLa0d6oxztz/dhZf31NGa8c7dXduiqhpAR/Y8yLIlIXc/lG4I8jv/8B8J+Eg/+NwL+a8AaDnSJSLiLzjTHvJmMsSmWqcC+enW71jlOa6R8M79595Wgvs3IkbnrHYQksq63goXX1WKKzfZXYVJZ6zvME9GPAvMjvFwJHPI9rj1yLIiLrRaRJRJo6OjqmcJhKpYcOf4C9R7rd20V5vhGPGS3wA/x845WIwJUPvMDGbXvJgD2cKkWmpc4/Msuf0F9DY8xmY0yjMaaxqqpqikam1PRKdKaubRs6+wNR16rLE1ffWIRTO945fX1NOXOK82lp6446sEWpeKay2ue4k84RkfnAicj1o0CN53HVkWtKzWiJKnmCQZubNu3gQHsPRbkW/cM2s3KFN070x32dkvwcnv38e5ldlEf9V5/FPxikKN/Hv92xEp/Piqrf13y/SmQqg/9Pgb8C7o/89yee63eJyHbCC709mu9X2SD2TN1T/UNUzMrlhodf5GCkPr9/2GAJnB6O/0X5gQ9fyk2XV2NZFh3+AAND4fWBwWGbrtNBqkry9ZQtNS7JKvXcBuwALhSRdhG5lXDQv05Efg9cG7kN8BTwFnAIeBT4TDLGoFS6iz37trIwl5s27XADvyNRU00L+GjjQizLcl+vMfJ6jZ5ZvpZ1qvFIVrXP2gR3XRPnsQa4Mxnvq1QmcXbVdvgDGAy/O+5nn2eBdyyz8nzYNvh80a+ns3x1NnSHr1JJEtuWId7ZugAbt7Ww+3DXGK82Uv9QiI9897f85M4r3XbLuktXnS0N/kolQexi7uOfXM6ax3ayr62booIzZ+t+8+bLzirwO1472qvtllVSaEtnpZLAu5jb1NrFRyLn59qcOVt3z9udXPsP/znma104r4gffCK660nd7FlYwOXnartllRw681cqCZzF3ObWLi6tLmN/1GYti9PDNhfNKxqxuBvPG8f7efiFt7AAm3Ab5mc+9166B0Oa21dJo8FfqSQIhQxf/tBiZhflRdXfF+ZanDe3hFeP9vLWyYFxv15LWzcv/fX7ae0cYPm5Ffh8Pqpy9Z+rSh7926TUJAWDNvVffQb/YJCSghx+9bn3MhAI9+MZGLbZ3x4+TD0UHP9rLltUwfyKWSyoLJyKISulwV+pyTrU0ec2X/MPBunsD3DpwjL2RYL+RD1995WcV1XMG8f8XDCv2K3rVyqZNPgrNUkXzCumpCAH/2CQ4nwfX/nZQXe2P1H1NWWcV1VMw9eedb9J7P3SdeTk6AeASi4N/kpNkFO/X16Qwx9O9nPBvGL2fuk6DnX0UT4rhyu+/p8T62IYsbS6jCc/vZo3j0d/kzjU0cdF80vHeLZSE6PBX6kJcOr597zdiUi4574zO79gXglrNu8gFNOfoTDXYjBoU5Drc3vxxFpaXcqTn16NZVlR3yRKCnK4YF7xdPxoKsto8FdqApx6fhvcJuXO7LyiKI/m1pEbuAaGbQQYGAqRKxCvZ9srR3vpHBimqiQfy7LcbxKa81dTRf9WKTUBc4rzaKgtj7pmCRTlW6z/we7w6VtxOJcTNOvkspryqM1bOTkWF80v1cCvpoz+zVJqHJxDWGzb8Hc3XIzPs9HKNnDV13/N/qP+Ec+7oGrWmK9dX1POjzas0s1balpp2kepGLEN2coLcrj50XCfnsI8H/1DIWblWgwkmsZ7vNlxetT7n7r7St4zv1QDv5p2GvyV8nAWdJtau8KBfjDIrHwf/ZFD1fsiC7YDw3ZS3m9OsfbdV6mhaR+lPJwF3ZBt8A8GscEN/MlQlOejsbYMn8DyugrtzqlSRmf+Snk4Ddqcmb9Tbz8eAiPq+wtzLc6fW+SuBwwGbR65pRHLEm3SplJKg79SHu5pW30BgsEQ133rNwlr82NZlmCMcY9hPG9OIU9/9iosy+KjkQPaGxdVMLdUUz0q9SR8qmJ6a2xsNE1NTakehpphYk/e8l5f++hO9hzuTHierpcz4y/Jz+GPqoqievo01Jbzow2rAfS4RTXtRKTZGNMY7z7N+aus5AT4Vfc9x5rNO7EjUd62DW8e94878MOZVM/AUJDvfnwZlyw804ph/5FuTvUP6aHqKu1o8FdZyXvyVnNrF8d7B3ntnR4+8p2Xuf7bvxl34HdYAo11lcwrLeDHG1ZzycJS95qevKXSkeb8VVZyduo2t3axtLqM6/7x1/SNs6rHOWGrKFLzD7BkYRnfXrMU2zZ87Pu7ef2dXpbWlLP1thU621dpSWf+KisZg7s423t6aNyBH8KB/+IFJTzz39+LLxLX97X3cMX9z3PTph00tXYRMnCgvYfOgeGp+QGUmiQN/ipr2LbheM8gJ3oHebdngD2t3RjgzY7xH6/oOPiOH58lNNZV4rPCnwAhE87xL6kuI8cSli2q0JSPSlua9lFZwbYNazbvZPfhTgBm5UwuFWOAjdv3sfXWFXQODHHX1hZa2rpZtqiCrbetoHNgWCt7VFrT4K+yQoc/ENVu+XTw7EqcC3Mtt7VD8+FOuk4PM7e0gO3rV0WVcurO3fSTqLQ3W2naR814tm24c0szoUnuafn7v1gc1dNnSXWZm9bRUs70lqi0dzzP6/AHyIT9UBOlwV/NGLH/UJ3bx3tP09TWPenX/5v/OBh1+7sfW6bBPg2MJ0DHlvae6h8a1+uezQdGptC0j5oRwjn9HTS3dtFQW86319Rz59YW9h3pYVZucuc4lhBp01CQ1NedyaYq5eIE6ObWLpYtqmDb7SuxrJGv7/Rsch43noX4eB8YidJ5mZhS0uCvZoTj/kF2Hw7n9Pe0drP6gRfc+5LVfhng8kXlPHLLMk3xTMB4A/TZ8AbopsOdnOwLxP1Qdno2xQbo0YK29wOjobYCYwzGmBGPm+zPl6oPDg3+KuPZtuEzW1om/To5QGwPz8sXVfDQ2npgZuf1pzIATWQGPRG2HQ7G9TVl7GntJmTgrm172Z4g+Dr//7zPHy1ou03+/AE2bmth9f3Px33cZH6+qfxgHEvKcv4icr2IvCEih0TknlSNQ2W+U/1D7E9CTj9I9D+Ip+++kh9uWMU55bM4p3wWc0sLZmzgn8rctjODHmvvg5O7D4XsMXP4zphX3/88QTucigNoOtzJG8d6x7VAO551AMsSLEtoaetO+Ljx/nxnM4apXHBOycxfRHzAI8B1QDuwR0R+aow5OPozlRqpvCCHWZ5WC2erMM/ikgVlbr3+RVlyvGIyZ+bxvkEkSrnEPm/toztpOtxJYX4OA0MhGhPMhJ3me02RQ3cOHO3hsuoy9h7pwTbwgQdfYmlNGU9uWI3Pl3h+G27xUUFzWxeXLixjdlFuwseNtl4wnp9vtDEkeu2p/laQqrTPcuCQMeYtABHZDtwIaPBXo7JtQ0dfAAFmF+Vxsm+IOx7fM+nAf9E5xfz8riuwLF/GLdxN1tkshsbjDVYNteU8tLbBPbvAm3KJ9wHhnqBmcA/QaWrtGpHDT/Qh8eCapay+/wW3nHffkR5u2rSDH21YnTBghlt82IRsw94j3azZvIvt68MBNnaMTnCvLMzlZN/Ivx+xKaVEP2e8MTy4th6BESnFqUqXOVIV/BcCRzy324EV3geIyHpgPUBtbe30jUylLds23Lzpt+xpDad4SvJz6B8KTrgDp9fL//N9bPy/+9l/pJtbvreHbbevzLoNWpOZuXp5g9Xuw12sfuD5EbP3RLNZ9wS1w50U5uXgDwQJ2Ya7trawff0q9/neD4mBQJBf3H0VF55TAsCyugp2v93pjsdpp53o/+ep/iFaPOnC5rZwgJ1dlBd3jImuxwvy3rOgl1SX8aM7Vo34FhLvz8L7R5+sD+VE0rbO3xiz2RjTaIxprKqqSvVwVBro6Au4gR/AH5hc4C/Jz8GX4+NAew8hw7jrv2eiZCxmO8HKaXYXipPHTpTjNgYeXFPPjnuv4ZnPn2mY19LWHfV8b369sa6SC88pQUQQEbbfvpIdX3w/lywowWLsdtqVhbksqS5zby+rLWdOcV7CMUZVFkW+lSRaLznVP+Smpfa2dXPTph0j1lLGyvc7H8o77r2G7etXJv2baKqC/1GgxnO7OnJNqcRiFr2cb/NnmwYdGA5hiZz1Yp2K5g1Wy8+tdP9MKwtz3UXLeIujTgC94oHn2bhtL1XF+TTWVcb9fzJWQPzcD/fz+rE+Lqsp59trlkbd5108tW3Dusd2ceBoL0V5PpxXMSb6A8Zb4umsEQDutxKnbUhsAK8szI3aX7LvSPeI4D6eheKprDBLVdpnD3C+iJxLOOivAdalaCwqzTlfq2MrHpyJ1NnO/gvzfMwuyktKykOFWZaEex15cuTrHtsVldqI/fM+2RcdQDsHhkf9f5Jo/cBNCUVy+Ffc/zyNdZVsuXUFp/qH2LjtTPO9B9fU03S4k5DBXS9yvmVUleQnLPF8aF09q+97jpAJP16EuKmZzoFhBjxtwi+rKXfv8445lX/3UhL8jTFBEbkL+CXgA75vjHktFWNR6clZ2MUY7tq6l5Yj3VzqOR4xGfoDQToHhqkqyc+6PP9UcwJ0vJlx7J93vNy20xzPtg0n+wJxg2NsznzrbSvC6waRD4CQCZd+3rR5Rzi1F5klNB3uxGAozM/BPxjEJ+EZvzd4JyrxnFsS/lbivKfzQREbwOcU53H5uZU0He7ksppyfrRhFSISN8+fql3DKdvkZYx5CngqVe+v0pfTqsHZsevYd6SHPIGhJJU8e2djamLGG5jGs2g52u7b0UodY3PmzjeGk30Bt8X2kuoy9nsCP0BhfjjsDTgVYiI8dfeV7vrBaGOPN1YRRgTwRD/TeCt4pmPzl+7wVWnH+QcSz9kG/ksWlHLw3V43RXTpwlKejMzG1MRMJDCNt5IoXqnkWIEyUXD2ttieXZTL2kd3uSkeCAd9S4RGz3NjA/9oY4831kQ/k1OO7Dx/vBU8U13mCRr8VZrw1u87C2t7EnwAnI1HP97A5354wC29e3LDKiwrbYvd0tpEA9N4g2WsyWyu8r7ntttXcqJ3kOv+8UX8geCE1nrOduyQ+ENyPO871WWeoMFfpYHYNM/liyom/Zr5FgQ8/dy6Twf1hK0kmY7ABOP71jCe4GxZgs9nMTAU3jw2ME1rPYk+JMcz5mTtvRiNBn+VcrFpnua2rtiqznG5vK6Cb960hANHe/jnl96mqa0HgOI8Hx986CUa6yojG2k08E/GdAQmx2Rm3l5zivOiFmqnY61nsh+SyfrZE5FMOKGmsbHRNDU1pXoYaooYY7h505mZ/5IFJbzyjp+J/s18z7wijnQN0udp9WAJYMAGcixhx73XaGVPlkpF6+RU9/kXkWZjTGO8+3Tmr6aVN7fvbF4REbavX8Xx3kE2PN7E/qO9Z/Xarx/vj7ptSXiXJ8a49d1a3ZO9pnomnS7vOV4a/NW0ic3tL6+rZPv6lUC4dUNnX+CsA3+skvwcnv38e5lbWoAx6CYupWJo8FfTJja3v+dwJ0e7+9nwRAuvveNPynssrSnjvg9fyoXnlLjVPPHqsJXKdhr81bRxGmntPRJeiDXAVV//dVJeW4ClteVawqnUOGnwV1PK26Zh47a97G8PH6h+Ognn6hblWQwGDctqK3h4Xf2MPWJRqamgwV9NmURtGpIR+AV4/gt/jGVZmstX6izo92M1ZZye5slkSTjwLz+3krmlBe72+UwoWVYqnWjwV1NmTnHeiE6cf3fDhZN+3ac/exXb16/EGKb04HGlZjIN/iopbNtwvHeQE72D7izcGMjL8bmPWVZTxn2/eGNS73NZdbnbhGusk5CUUolpzl9NWuzZupcvquDhdQ0YY9jjyff7B4cITO6cdb77sYaonunT0WNGqZlIg7+atOP+waizdfe0hg/vfk/kYG3Hmx2nJ/S6s3Itzpszi1feDe/cvbyugrmlBe7909ljRqmZRoO/mhTbNnxmS8uI6yHb8Oo7Z3brFvhgcAKzfgEuWVjGtttWcmpgKKodhFc6b59XKp1p8FeT0uEPsP9I95iPGwxBUZ7PPS91LAbY29ZN1+lh5nlm+0qp5NDgr86abRvu3NI87gPUxxv4nUOhNI+v1NTR4K/O2vHeQZraxp71J+IcuHLRvCKK83NoOdLD4gWl/HjDKroHQ5rHV2oKafBXZ617YHKllQEb6mvL+dEdq9zSTSfgV+XqX02lppLW+auzduE5JRTnn6njv2DOxHPzr7T30Dkw7C7c6kxfqemhwV+NybYNHf6Au3nrzG34f3dfRWFe+APg9ycHJ/S6FprXVypV9Lu1GlUwaHPT5h0caO+hcVEFj39yOTc/upN9R7opyvfRHwi5xy2OZ923MNdiMGizrLaCR25p0Nm+UimiwV8lZNuGv9y8g72RRd09hzu54Tsv87tj4YNX+ia4XdcCLl5YxsNrG5hbGg76tm042RfQxV2lppkGf5XQqf6hqBp+2+AG/rNhAy2tXViWuIF/7aM73fYM225fiWXpB4BS00Fz/goYmdcHKC/IYfGC0lGeNbYL5xXh8wT0y2rK3Ry/NmZTKnU0+Ct3Bu5tjRwM2jR89VleOdpLYZ6PhpqJfwhsvXU5T3/2vTQuqsBnSbisc8OqEY3ZcizRhV+lppmmfZR76ErINjRFZuAd/kH8gSAAA0MhBodtHlx7GXdv2x/1XCH+Qm9Jfg4r/2g2lmUlbL6mjdmUSh2d+SsqC3Pdcs3CXB+hkE2Zp34f4OCxvhGBH+IH/ksWlLL3f13rHqQ+Wg2/1vcrlRo681ec7BuiPzLL9weCXPH1F0a0Yx5N7Oz/9WN+uk4HqSrxJXqKUirFJjXzF5GbROQ1EbFFpDHmvntF5JCIvCEif+q5fn3k2iERuWcy76/OjndxNxi0uePxpqjmbLHtmMfy+K2Xc/micve25u+VSn+Tnfm/CnwE2OS9KCKLgTXAxcAC4FkRuSBy9yPAdUA7sEdEfmqMOTjJcahx8pZXNtRWMBQMsa+9x72/vqacA0d7CMVp1VmYa3F62GbZonJ+d8zv1vl/65k32b5+9ah995VS6WVSM39jzOvGmHiHst4IbDfGBIwxbwOHgOWRX4eMMW8ZY4aA7ZHHqmkSVV7Z1hUV+AG+9KGLRgT+PAte/PyVXLygDMsSLMvi6c9d5d7f1NbDqYEh5pUWMLe0QAO/UhlgqhZ8FwJHPLfbI9cSXR9BRNaLSJOINHV0dEzRMGeueHX7EF1e2VBbTmyY/q/f2TnitYZsuDoibU0AAA6SSURBVP6h39LcFq4Iaj7cSd/pYNRjNNwrlVnGTPuIyLPAOXHu+ltjzE+SP6QwY8xmYDNAY2PjOI8LUcCoO2e95ZUn/YN84MGXxvWaA8M2hXk+BoZCFObncP7cYpbXVdLcFn4PPUpRqcwyZvA3xlx7Fq97FKjx3K6OXGOU6ypJYnfOdvgDWJZQWZhL58Awc4rzqCrJp2JWjnu0YmGexcCQPerrDkRO4hoYCtF1Osj29Vqjr1SmmqpSz58CW0Xkm4QXfM8HdhPODpwvIucSDvprgHVTNIas5aR2nEXdjdtaaDrcxax8H6eHQlxeV8njn1zOTZtedo9WtOMs8DYuKucrN1zMhx56GedjwWcJS6rLmF2Uq4enK5XBJhX8ReTDwENAFfALEdlnjPlTY8xrIvJD4CAQBO40xoQiz7kL+CXgA75vjHltUj+BGsGb2jHGsOq+57CB/kh1zu63O/mzh17kUMeA+5zBYHTw/8XGK1i8oIxg0GZWpHVzUZ7FBfNKOHCkm7WP7tJGbEplsEkFf2PMj4EfJ7jva8DX4lx/CnhqMu+rxubMyo0xXFZTTkvMWbvewA/RG7WW11Vw0TmlnOgNsOGJJvdD4/SwzYGjvYQMbiM2nfkrlZm0vcMMZwx8Z10D9TXl+CxhVq4VtyWDc80n8ODaetY9tovVDzzP3iNnSkEXzy9hWW25NmJTagbQ4D/D2LbheO8gJ3oHCQSC3PjIS1zx9RfIseAHn7yc08PxF3WL8n34BBrrKrFEaI40eoPwt4KifB8H3w338n/5i1ezff1KXeRVKoNpb58ZxLYNazbvYPfhLiA6lbOntZuPfW93wueeHgrx1N1XcWGkp493wfgrNyzmgw+/jG2gpa3bPYxFKZW5NPjPIE6Jp2OszRFFeT7Oqyrk1Xf8NNZVcuE5JW5Q97ZaBmiMfBhoukepmUGD/wwypziPhtoK9ng+AEZzejhEfm4uv733GubG9OOJLePUvvtKzSya888AiVo1xD7mRG8AMOEcfd6ZdspFeb647RfCaZwuLBk7jaN995WaWXTmn+bGc8i5bRtu3vRb9rSeKeccGArxxK2Xc/7cEuYU5/PmcX9UK4f6mnJeOdqjaRylspQG/zQX75Dz2UV5nOofcts1DIdCUYEfwvn+j31vD8vrKti+fhUXzS+N6sWz7bYVbqsHnc0rlX00+KcJ2zZxc+qxrRpCIZs1m3fQ3NpFYX4OA0MhLpxbmPB1vZuxYnvx6AYtpbKXBv80MJ4unB3+ABu3tXDF119w6+/9g+G2ygeP9Ue9niW4J3M11Ja7aR3txaOUcuiCbxqIl9rxCh+gIrS0dcc9Ycvr/KpZ/OZ/vI+l1WVYAmJZjLJOrJTKUhr804D3gJVEC7DOY3yWRFXyeF2yoISKogLe+w8vsq+9J1zNE+fDRCmlNO2TBrxdOBMtwIoIW25dwZ7Dnax7bJd73QJsoDDPYtMtDVzxjV+fuU/0MHWlVHwa/NOENx8fDNoc6ujjgnnFWFb4y5ltG2753i6aWrsoLsihPxDk4gWlvHK0F4CBIZs7tuyNes2f3RVuy6zVPEqpWBr800wwaFP/1WfwDwYpKchh75euIyfH4lT/EE2RZmsDQyEev3U551UVseGJFrfz5uvH/FGvNadYN2UppeLTnH+aefOE363i8Q8GOdTRB0BlYS6FkVy/MYZbHtvNivteIMcSt13zskUVNNaWu6+1cfu+uCd0KaWUzvzTiG0bvvyTV93bhbkW51WFa/g7B4YZCIQ/FLzxvOVID7+952osEeYU53GiN8DqB54nZBt3sVfLO5VSsXTmn0ZO9Q9FHZ4yMGxT/9XnCAZt5hTn0VhXiU+gOP9Mtc+yRRXMLcl3++7MLc2ncYzKIaWU0pl/GnB2984uymXZogr2vN3pHpjupH4uml/qVgRVFuZysn8IgRHN1sZTOaSUUhr8U8y7u7ehtpwH19QDhuu+9Rt30feCecVAdEXQvNKChK+ngV8pNRYN/tMkUVD27u7dfbiLK77+QvjglL+5lrdO9UeVe47nPcbqAKqUUqA5/2nhBOVV9z3Hms07oypw3J27kRgdsg1NrV30BIJcNL903IEfxm4ToZRSDg3+02C0oOzk6F++52pKCsJfxArzfFQW5k74fcbTJkIppUDTPtOisjCXJdVl7D/SHTcoW5bgsyy3lHMgEKRzYHjCJZq62KuUGi+d+U8x2zase2wX+9t7uLS6jAfXLI37OKeUM8cSGusqz3rWrsctKqXGQ2f+U8xJ+YRsw74jPay+/wWW1VWwPWYxVmftSqnppDP/KeA9cN3JwztxPmQMu9/upMMfGPE8nbUrpaaLBv8ki63sMQa23b6Sn911RdTjnPju/aBQSqnpommfJEt04PpXfnbQfczliyqoKsnXunylVMrozD/JYsstKwtzefO4n+bWLgB8Ao/c0oCIaF2+UipldOafZN6F28rCXNY9Fj6ApTA/h4FAkMa6SreE0/mgcGb+WpevlJoukwr+IvIN4EPAEPAH4JPGmO7IffcCtwIh4G5jzC8j168Hvg34gMeMMfdPZgzpyFm47fAH3EqfgaEQv7j7Ki48p8Rd0NUKH6VUqkw27fMMcIkxZgnwJnAvgIgsBtYAFwPXA98REZ+I+IBHgA8Ai4G1kcfOGPEqfXIsoXFRRVTgd2iFj1IqFSY18zfG/Mpzcyfw0cjvbwS2G2MCwNsicghYHrnvkDHmLQAR2R557EFmgOgOnRU8tK6erbetoHNgWGf2Sqm0kswF308BT0d+vxA44rmvPXIt0fURRGS9iDSJSFNHR0cShzl1ojt0drL6vudY99guZhdp4FdKpZcxg7+IPCsir8b5daPnMX8LBIEtyRqYMWazMabRGNNYVVWVrJedUm6Hzki5ZsigVTxKqbQ0ZtrHGHPtaPeLyCeADwLXmDM7lY4CNZ6HVUeuMcr1jDDaYSnOAu7JvgB3bW2hpS1+IzellEq1yVb7XA/8NfA+Y8yA566fAltF5JvAAuB8YDcgwPkici7hoL8GWDeZMUyn8WzKsixhbmkB29ev0ioepVTammyd/8NAPvBMJMDtNMZsMMa8JiI/JLyQGwTuNMaEAETkLuCXhEs9v2+MeW2SY5hS3pl+vE1Zidoue49cVEqpdDPZap/zRrnva8DX4lx/CnhqMu+bbIlSObEz/a23rdBNWUqpGSHrd/iOlsqJnel3Dgzrpiyl1IyQ9b19RuuvE+9YRN2UpZSaCbJ+5u/tr9NQW4ExhlDIdjdmbbl1BYc6+rhgXrEGfKXUjJH1wd8pz+zwB9i4rYVV9z0XbsI2FGJZbQVg3JLNLbeuoOu07tZVSmW+rA/+EK7MsSyhpa2bkAH/YPgg9ea2LjCGkIGmw53ctHkHr7T3aO99pVTGy/qcv8PdnStQUpCDL5Lnd3L+l9WUc6C9R3vvK6VmBJ35R8T24Xdy/sYQOY0rl7WP7tIyT6XUjKDB38O7Mcv5r8iZ32uZp1JqptDgPwG6a1cpNVNozl8ppbKQBn+llMpCGvyVUioLafBXSqkspMFfKaWykAZ/pZTKQhr8lVIqC2nwV0qpLDTjg79tGzr8Ac6cLa+UUmpG7/Adz4HrSimVjWb0zH+0U7qUUiqbzejgH+8YRqWUUjM87eNt06ydOJVS6owZHfxBO3EqpVQ8Mzrto5RSKj4N/koplYU0+CulVBbS4K+UUllIg79SSmUhDf5KKZWFJBN63ohIB9CahJeaA5xMwutMNR1n8mXKWDNlnJA5Y83mcS4yxlTFuyMjgn+yiEiTMaYx1eMYi44z+TJlrJkyTsicseo449O0j1JKZSEN/koplYWyLfhvTvUAxknHmXyZMtZMGSdkzlh1nHFkVc5fKaVUWLbN/JVSSqHBXymlslJWBX8R+T8ickBE9onIr0RkQarHlIiIfENEfhcZ749FpDzVY4pHRG4SkddExBaRtCunE5HrReQNETkkIvekejyJiMj3ReSEiLya6rGMRkRqROQFETkY+f/+2VSPKRERKRCR3SKyPzLWr6R6TKMREZ+I7BWRn0/H+2VV8Ae+YYxZYoxZCvwc+N+pHtAongEuMcYsAd4E7k3xeBJ5FfgI8GKqBxJLRHzAI8AHgMXAWhFZnNpRJfQvwPWpHsQ4BIEvGGMWAyuBO9P4zzQAXG2MuQxYClwvIitTPKbRfBZ4fbreLKuCvzGm13OzCEjb1W5jzK+MMcHIzZ1AdSrHk4gx5nVjzBupHkcCy4FDxpi3jDFDwHbgxhSPKS5jzItAZ6rHMRZjzLvGmJbI7/2Eg9XC1I4qPhPWF7mZG/mVlv/mRaQa+HPgsel6z6wK/gAi8jUROQLcQnrP/L0+BTyd6kFkoIXAEc/tdtI0UGUiEakD6oFdqR1JYpFUyj7gBPCMMSZdx/ot4K8Be7recMYFfxF5VkRejfPrRgBjzN8aY2qALcBd6TzWyGP+lvBX7S3pPE6VXUSkGHgS+FzMN+q0YowJRdK81cByEbkk1WOKJSIfBE4YY5qn831n3Bm+xphrx/nQLcBTwJencDijGmusIvIJ4IPANSaFGzIm8Geabo4CNZ7b1ZFrahJEJJdw4N9ijPn3VI9nPIwx3SLyAuF1lXRbVL8CuEFE/gwoAEpF5AljzMem8k1n3Mx/NCJyvufmjcDvUjWWsYjI9YS/Bt5gjBlI9Xgy1B7gfBE5V0TygDXAT1M8powmIgJ8D3jdGPPNVI9nNCJS5VTJicgs4DrS8N+8MeZeY0y1MaaO8N/R56c68EOWBX/g/ki64gDwJ4RX19PVw0AJ8EykNPW7qR5QPCLyYRFpB1YBvxCRX6Z6TI7IgvldwC8JL0z+0BjzWmpHFZ+IbAN2ABeKSLuI3JrqMSVwBfBx4OrI38t9kRlrOpoPvBD5976HcM5/WsooM4G2d1BKqSyUbTN/pZRSaPBXSqmspMFfKaWykAZ/pZTKQhr8lVIqC2nwV0qpLKTBXymlstD/B4Kaa+4btCZoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsHIyjcHpTjs"
      },
      "source": [
        "ransac_pred = ransac.predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7XUeXQ4paBr"
      },
      "source": [
        "lr_pred = lr.predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HZ_jdupZpcvp",
        "outputId": "63f12b0e-1792-4c71-c93a-0a6a5090d0d1"
      },
      "source": [
        "plt.scatter(X,y,s=5,label = \"data\")\n",
        "plt.scatter(X,ransac_pred,s=5,label = \"ransac\")\n",
        "plt.scatter(X,lr_pred,s=5,label = \"Linear regression\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQicLP1dp4Pb"
      },
      "source": [
        "**Polynomial Regression**\n",
        "\n",
        "* Sometimes relationship between variables & target is of higher polynomial degree\n",
        "* Transformer can be used to convert data to higher degree\n",
        "* Linear models can predict coef of these higher degree polynomials"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cy8L7g9pvW2"
      },
      "source": [
        "from sklearn.datasets import make_circles"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwepgjFIqDde"
      },
      "source": [
        "X,y = make_circles(n_samples=1000,noise = .04)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wd0V5SGWQs6N"
      },
      "source": [
        "**Polynomial Basis intro**\n",
        "\n",
        "\n",
        "Every row represents a dependent data point.\n",
        "\n",
        "If feature-dimension is 2\n",
        "- order = 1 -----> $[1,X_1,X_2]$\n",
        "- order = 2 -----> $[1,X_1,X_2,X_1^2,X_1X_2,X_2^2]$\n",
        "- order = 3 -----> $[1,X_1,X_2,X_1 ^2,X_1X_2,X_2^2,X_1^3,X_1^2X_2,X_1X_2^2,X_2^3]$\n",
        "\n",
        "If feature-dimension is 3\n",
        "- order = 1 -----> $[1,X_1,X_2,X_3]$\n",
        "- order = 2 -----> $[1,X_1,X_2,X_3,X_1^2,X_1X_2,X_1X_3,X_2^2,X_2X_3,X_3^2]$\n",
        "- order = 3 -----> \n",
        "$[1,X_1,X_2,X_3,X_1^2,X_1X_2,X_1X_3,X_2^2,X_2X_3,X_3^2,X_1^3,X_1^2X_2,X_1^2X_3,X_1X_2^2,X_1X_2X_3,X_1X_3^2,X_2^3,X_2^2X_3,X_2X_3^2,X_3^3]$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INfpVoSCV7sL",
        "outputId": "c226debc-991e-4b92-d401-1ed2999c4ab4"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "q = np.arange(6).reshape(3,2)\n",
        "print(q)\n",
        "poly_1 = PolynomialFeatures(degree=1)\n",
        "poly_2 = PolynomialFeatures(degree=2)\n",
        "poly_3 = PolynomialFeatures(degree=3)\n",
        "res_1 = poly_1.fit_transform(q)\n",
        "res_2 = poly_2.fit_transform(q)\n",
        "res_3 = poly_3.fit_transform(q)\n",
        "print(res_1)\n",
        "print(res_2)\n",
        "print(res_3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 1]\n",
            " [2 3]\n",
            " [4 5]]\n",
            "[[1. 0. 1.]\n",
            " [1. 2. 3.]\n",
            " [1. 4. 5.]]\n",
            "[[ 1.  0.  1.  0.  0.  1.]\n",
            " [ 1.  2.  3.  4.  6.  9.]\n",
            " [ 1.  4.  5. 16. 20. 25.]]\n",
            "[[  1.   0.   1.   0.   0.   1.   0.   0.   0.   1.]\n",
            " [  1.   2.   3.   4.   6.   9.   8.  12.  18.  27.]\n",
            " [  1.   4.   5.  16.  20.  25.  64.  80. 100. 125.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "NAHM7R78qIda",
        "outputId": "460d38b0-135b-4558-8323-0d6d23581951"
      },
      "source": [
        "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXzqeUoXqOxQ"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26WjxZEOqS2z"
      },
      "source": [
        "pol = PolynomialFeatures(degree = 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4UfQW-QqV3K"
      },
      "source": [
        "X_tf = pol.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86sEs1iXqehH"
      },
      "source": [
        "lr = LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6BphpxAqgS4"
      },
      "source": [
        "trainX,testX,trainY,testY = train_test_split(X_tf,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTLj_bnaqqmB",
        "outputId": "d388fcd4-92cb-4700-980f-35ecf59fe2ab"
      },
      "source": [
        "lr.fit(trainX,trainY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yr3SsH7LqtNf",
        "outputId": "dbd2b0d3-c328-4856-8238-c9f82679a640"
      },
      "source": [
        "lr.score(testX,testY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.996"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhDq78QMqwoH",
        "outputId": "ab37fab5-4fa0-4600-f230-9849147debe2"
      },
      "source": [
        "lr.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.13886114e-04, -4.92207798e-02,  4.62984031e-02,\n",
              "        -9.48481162e+00, -1.29156437e-01, -9.35699000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccItz44irXxP"
      },
      "source": [
        " ***Bias Variance***\n",
        "\n",
        "**Bias**\n",
        "* Fitting training data poorly, but produce similar result outside training data\n",
        "* we are building simple models that predicts terribly far from the reality but they don't change much from dataset to dataset.\n",
        "* Situation of underfitting.\n",
        "* a linear regression model would have high bias when trying to model a non-linear relationship.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE9Q8kAkrq2L"
      },
      "source": [
        "**Variance**\n",
        "* Building complex model that fits the training data well but many not work similar way of other dataset.\n",
        "* Model is not generalized & is overfitting.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNrGF7Ourx15"
      },
      "source": [
        "**Bias Variance TradeOff**\n",
        "* Increasing the accuracy of the model will lead to less generalization of pattern outside training data.\n",
        "* Increasing the bias will decrease the variance.\n",
        "* Increasing the variance will decrease the bias.\n",
        "* We have to get perfect balance of bias & variance\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjphDxE9Ikor"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7Cdbkfjqyz9"
      },
      "source": [
        "# generate 10000 points following y = 2x(-1,1) + 3 + 0.1 * n(0,1) \n",
        "def func(x,n):\n",
        "    return 2 * x + 3 + 0.1 * n\n",
        "n = np.random.rand(10000,1) # -->[0.,1)\n",
        "X = -1 + 2 * np.random.rand(10000,1) # -->[0.,1)\n",
        "y = func(X,n)\n",
        "\n",
        "# if needed,try them\n",
        "\n",
        "# print(X[:10])\n",
        "# print(n[:10])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPq9InIKIQFz"
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeYuMoxVPMAe"
      },
      "source": [
        "lr = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xO7QXL78PZA6"
      },
      "source": [
        "lr_1 = lr.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zm0XL-hUQilw"
      },
      "source": [
        "y_pred = lr_1.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ceMgub2QQvqG",
        "outputId": "db46c06c-e62c-4cd4-c489-5a8d96ac6bd1"
      },
      "source": [
        "plt.scatter(X_test,y_test,s=5,label = \"original data\")\n",
        "plt.scatter(X_test,y_pred,s=5,label =\"linear\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m46tRIgdRDP3",
        "outputId": "3c42bb39-3c8a-49c8-e1c2-e8cf376b365e"
      },
      "source": [
        "lr_1.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9993927418441865"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWllA-NpPdl_",
        "outputId": "a5a61c5e-c359-428f-e9a1-f44c0667b57a"
      },
      "source": [
        "# weight\n",
        "lr_1.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.9991295]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QnALJZjvSz6p",
        "outputId": "687f8087-cd69-4b53-b7c6-f0386f475cfe"
      },
      "source": [
        "# bias\n",
        "lr_1.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.05021312])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Phno69PTLPHl"
      },
      "source": [
        "Generate 1000 points $y = 2x^2(-2,2) + 5 + N(0,1)$ "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsSzxkeSUo4D"
      },
      "source": [
        "def func_square(x,n):\n",
        "    return 2 * (x ** 2) + 5 + n\n",
        "\n",
        "N = np.random.rand(10000,1)\n",
        "X_ = np.random.rand(10000,1)\n",
        "X_ = -2 + 4 * X_\n",
        "y_ = func_square(X_,N)\n",
        "# if needed,try them\n",
        "\n",
        "# print(X_[:10])\n",
        "# print(N[:10])\n",
        "# print(y_[:10])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xTyHQCMOEli"
      },
      "source": [
        "X_train_,X_test_,y_train_,y_test_ = train_test_split(X_,y_,test_size = 0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOLDLdg6MpBg"
      },
      "source": [
        "lr2 = lr.fit(X_train_,y_train_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KkaOAm9INYVW",
        "outputId": "b7d3df70-74e6-4f9a-b95e-2fa661a1202d"
      },
      "source": [
        "# Linear sucks\n",
        "print(lr2.score(X_test_,y_test_))\n",
        "y_pred_linear = lr.predict(X_test_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.0009945453949153915\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLJpl-SYOSvV"
      },
      "source": [
        "# polynomial basis\n",
        "# note we only transform x \n",
        "pol = PolynomialFeatures(degree=2)\n",
        "X_train_3 = pol.fit_transform(X_train_)\n",
        "X_test_3 = pol.fit_transform(X_test_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEhb3fbuPXQC",
        "outputId": "950df1a2-acab-4d87-c8b9-d259dc90e43b"
      },
      "source": [
        "# 第一列为1，对结果无任何影响，故第一个coefficent为0\n",
        "lr3 = lr.fit(X_train_3,y_train_)\n",
        "lr3.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.00363362, 2.00456387]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 404
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBWZduoAPiZd",
        "outputId": "0b3e3b2d-67d6-4b6c-b009-c104648f1c8d"
      },
      "source": [
        "print(lr3.score(X_test_3,y_test_))\n",
        "y_pred = lr.predict(X_test_3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9854533731382091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yU301BRSi8iv"
      },
      "source": [
        "Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inZIrWb9UXSa",
        "outputId": "447cdf3d-4615-484c-da1f-748a4ca59f74"
      },
      "source": [
        "# lasso do out-or-not job,arbitarily choose variable\n",
        "la = Lasso().fit(X_train_3,y_train_)\n",
        "la.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        , -0.        ,  1.31312786])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 406
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGEZAqCehUe_",
        "outputId": "ae60aa97-ad6f-4e2f-fd2c-f78f53f7ceae"
      },
      "source": [
        "y_pred_lasso = la.predict(X_test_3)\n",
        "la.score(X_test_3,y_test_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8718388514745457"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 407
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-FYTbUhjCrS"
      },
      "source": [
        "Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QENNFSmZjFlt",
        "outputId": "8385ff5b-ae34-4dac-d7fc-604e1826c2e9"
      },
      "source": [
        "ridge = Ridge(alpha = 0.8).fit(X_train_3,y_train_)\n",
        "y_pred_ridge = ridge.predict(X_test_3)\n",
        "print(ridge.score(X_test_3,y_test_))\n",
        "ridge.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9854544976588081\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.00363192, 2.00442527]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 408
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KsM1BdeyiTmB",
        "outputId": "8a0f5955-d94d-40d8-9f84-f6dc3d083d5c"
      },
      "source": [
        "plt.scatter(X_test_,y_test_,s=5,label = \"original\")\n",
        "plt.scatter(X_test_,y_pred,s=5,c = \"r\",label = \"Polynomial Regression\")\n",
        "plt.scatter(X_test_,y_pred_linear,s=5,label = \"naive Regression\")\n",
        "plt.scatter(X_test_,y_pred_lasso,s=5,label=\"lasso\")\n",
        "plt.scatter(X_test_,y_pred_ridge,s=5,label = \"ridge\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dwPrVpfij7d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}